diff --git a/master/.buildinfo b/master/.buildinfo index 130d6ba05..fe9891c8c 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: 6c1b6ba46e7b82b74029d5189564c63b +config: 6aec967625063143b64e56727739db11 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 3787fc9c4..0c53b3ea4 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 ed7d71081..314f3c3f3 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 c92fe8408..410e92cec 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 c3e898c86..7a6d34646 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 86f86f761..6adda08f1 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 18d77c2f9..f6b57f506 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 59f0cd390..4aa91d901 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 2e4967927..5b1d4b109 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 21ed5b2b5..1c8f69409 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 39b5e1c48..02b4cea44 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 bc551b6d9..b66659c26 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 196394de9..a4dc1ae2a 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 575a5fa2a..d6884b9b5 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/adapter/imagelab.doctree b/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree index d1047312b..5e178d1b4 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree and b/master/.doctrees/cleanlab/datalab/internal/adapter/imagelab.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree b/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree index 9f68746b5..7b1d2b072 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/adapter/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index d54999be1..1c1d28ad2 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 625aa8a6f..a552c19e8 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 e38897667..ca771b861 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 e6c2d457a..c90b8b09b 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 a58ccd363..9e3bf2a74 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 72c409c2c..4476af8d1 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 2129f3be7..d4e4e95dd 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 a60a9643e..11d9e0e3f 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 876995248..0f4de1af3 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 bbac177f3..f9bd0e58a 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 4b3e332e4..73af6c488 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 803371b43..ab1f1b5a9 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 5e1717e32..a407495b9 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 04e4f5607..fb7211446 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 a365698a6..0a77cea1a 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 092bf199b..b2234e9b1 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 9a9a216c6..89eca941a 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 d74d3d7f3..d48834319 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 8c745bcc3..37b38bff1 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 a6ff95b19..8058dda65 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 858ba07dc..02d7152bc 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 3fab22896..d94aa6dc0 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 4291e697f..1fa841228 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 765e6dc88..10b272551 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 e50f79695..23497f24f 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 e18905766..2e841753c 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 f61266729..ec099fe9f 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 54f40e282..a3e5829de 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 6f2be0e04..6af8dbf6d 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 06d6e3549..618aed9f9 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 2f528220d..f9295b7cf 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 15d730a57..656e4cdbc 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 7791925f8..8058306d1 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 f6fdb4ecf..0b1bcc68b 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 e0840fca3..79fa01e15 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 3db4d59f3..e0c18601c 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 342086dd7..0204efe7a 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 448ec3015..794cbd0a1 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 3c22507a6..51383148f 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 a9dc9f30f..bbfdbc395 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 c7a519e77..a9dc5241a 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 0ef7889da..05b60b9e6 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 6ea814e66..de4c3a0db 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 a449a7820..3652deca2 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 d7db46d31..ede9d0492 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 3dec2946a..2fe2cff3f 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/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 1b80f4a60..95aa89b4a 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 39957fbec..37b082137 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 70b4faea9..58fc781e7 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 ee1cd6aa5..60255825b 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 dd2c4c966..205a019af 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 17a00313a..ccf4594cc 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 f415b5e85..92e7600b8 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 74389f8ee..2624a169f 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 b7a8e2b0b..14b44039c 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 91629228c..321a65e78 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 bcec63f15..d93158e13 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 2d2f7ea83..280465154 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 50a7b4c11..572453a64 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 04972d6d0..3895b507b 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 15354ab29..83a68c502 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 0f297ab86..42288f642 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 8c4d286fa..39e5dd167 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 7fc045089..1bc062de5 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 1d8062f6d..533e90279 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 4c3a712f2..886981360 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 fee804d08..462bbbb3d 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 b4388ecf7..736561a58 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 4f0395bb0..574dafbf9 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 e15c33d9e..52cb44abf 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 6b7ac1d7e..0646076ad 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 fd393aed1..36e9a681a 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 aa91f574d..26fb434c1 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 9d1517aad..1310c3072 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": "2024-09-06T19:32:51.069638Z", - "iopub.status.busy": "2024-09-06T19:32:51.069457Z", - "iopub.status.idle": "2024-09-06T19:32:52.310694Z", - "shell.execute_reply": "2024-09-06T19:32:52.310136Z" + "iopub.execute_input": "2024-09-26T14:46:49.976999Z", + "iopub.status.busy": "2024-09-26T14:46:49.976816Z", + "iopub.status.idle": "2024-09-26T14:46:51.290105Z", + "shell.execute_reply": "2024-09-26T14:46:51.289537Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:32:52.313494Z", - "iopub.status.busy": "2024-09-06T19:32:52.312922Z", - "iopub.status.idle": "2024-09-06T19:32:52.331174Z", - "shell.execute_reply": "2024-09-06T19:32:52.330732Z" + "iopub.execute_input": "2024-09-26T14:46:51.292353Z", + "iopub.status.busy": "2024-09-26T14:46:51.291898Z", + "iopub.status.idle": "2024-09-26T14:46:51.324181Z", + "shell.execute_reply": "2024-09-26T14:46:51.323699Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:52.333414Z", - "iopub.status.busy": "2024-09-06T19:32:52.333012Z", - "iopub.status.idle": "2024-09-06T19:32:52.616135Z", - "shell.execute_reply": "2024-09-06T19:32:52.615552Z" + "iopub.execute_input": "2024-09-26T14:46:51.326351Z", + "iopub.status.busy": "2024-09-26T14:46:51.325915Z", + "iopub.status.idle": "2024-09-26T14:46:51.516296Z", + "shell.execute_reply": "2024-09-26T14:46:51.515685Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:52.647632Z", - "iopub.status.busy": "2024-09-06T19:32:52.647448Z", - "iopub.status.idle": "2024-09-06T19:32:52.650810Z", - "shell.execute_reply": "2024-09-06T19:32:52.650339Z" + "iopub.execute_input": "2024-09-26T14:46:51.550190Z", + "iopub.status.busy": "2024-09-26T14:46:51.549718Z", + "iopub.status.idle": "2024-09-26T14:46:51.556194Z", + "shell.execute_reply": "2024-09-26T14:46:51.555704Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:52.652810Z", - "iopub.status.busy": "2024-09-06T19:32:52.652474Z", - "iopub.status.idle": "2024-09-06T19:32:52.660488Z", - "shell.execute_reply": "2024-09-06T19:32:52.660065Z" + "iopub.execute_input": "2024-09-26T14:46:51.558094Z", + "iopub.status.busy": "2024-09-26T14:46:51.557801Z", + "iopub.status.idle": "2024-09-26T14:46:51.566504Z", + "shell.execute_reply": "2024-09-26T14:46:51.566057Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:52.662789Z", - "iopub.status.busy": "2024-09-06T19:32:52.662453Z", - "iopub.status.idle": "2024-09-06T19:32:52.664910Z", - "shell.execute_reply": "2024-09-06T19:32:52.664468Z" + "iopub.execute_input": "2024-09-26T14:46:51.568613Z", + "iopub.status.busy": "2024-09-26T14:46:51.568153Z", + "iopub.status.idle": "2024-09-26T14:46:51.571064Z", + "shell.execute_reply": "2024-09-26T14:46:51.570501Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:52.667005Z", - "iopub.status.busy": "2024-09-06T19:32:52.666677Z", - "iopub.status.idle": "2024-09-06T19:32:53.186834Z", - "shell.execute_reply": "2024-09-06T19:32:53.186291Z" + "iopub.execute_input": "2024-09-26T14:46:51.573000Z", + "iopub.status.busy": "2024-09-26T14:46:51.572679Z", + "iopub.status.idle": "2024-09-26T14:46:52.105207Z", + "shell.execute_reply": "2024-09-26T14:46:52.104691Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:53.189445Z", - "iopub.status.busy": "2024-09-06T19:32:53.189066Z", - "iopub.status.idle": "2024-09-06T19:32:55.090605Z", - "shell.execute_reply": "2024-09-06T19:32:55.089933Z" + "iopub.execute_input": "2024-09-26T14:46:52.107319Z", + "iopub.status.busy": "2024-09-26T14:46:52.107018Z", + "iopub.status.idle": "2024-09-26T14:46:54.109749Z", + "shell.execute_reply": "2024-09-26T14:46:54.109008Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.093443Z", - "iopub.status.busy": "2024-09-06T19:32:55.092787Z", - "iopub.status.idle": "2024-09-06T19:32:55.103390Z", - "shell.execute_reply": "2024-09-06T19:32:55.102831Z" + "iopub.execute_input": "2024-09-26T14:46:54.112432Z", + "iopub.status.busy": "2024-09-26T14:46:54.111599Z", + "iopub.status.idle": "2024-09-26T14:46:54.122786Z", + "shell.execute_reply": "2024-09-26T14:46:54.122299Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.105571Z", - "iopub.status.busy": "2024-09-06T19:32:55.105237Z", - "iopub.status.idle": "2024-09-06T19:32:55.109432Z", - "shell.execute_reply": "2024-09-06T19:32:55.108857Z" + "iopub.execute_input": "2024-09-26T14:46:54.124636Z", + "iopub.status.busy": "2024-09-26T14:46:54.124305Z", + "iopub.status.idle": "2024-09-26T14:46:54.128711Z", + "shell.execute_reply": "2024-09-26T14:46:54.128162Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.111438Z", - "iopub.status.busy": "2024-09-06T19:32:55.111142Z", - "iopub.status.idle": "2024-09-06T19:32:55.120139Z", - "shell.execute_reply": "2024-09-06T19:32:55.119708Z" + "iopub.execute_input": "2024-09-26T14:46:54.130465Z", + "iopub.status.busy": "2024-09-26T14:46:54.130125Z", + "iopub.status.idle": "2024-09-26T14:46:54.138888Z", + "shell.execute_reply": "2024-09-26T14:46:54.138403Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.122107Z", - "iopub.status.busy": "2024-09-06T19:32:55.121935Z", - "iopub.status.idle": "2024-09-06T19:32:55.235206Z", - "shell.execute_reply": "2024-09-06T19:32:55.234622Z" + "iopub.execute_input": "2024-09-26T14:46:54.140677Z", + "iopub.status.busy": "2024-09-26T14:46:54.140309Z", + "iopub.status.idle": "2024-09-26T14:46:54.256974Z", + "shell.execute_reply": "2024-09-26T14:46:54.256488Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.237464Z", - "iopub.status.busy": "2024-09-06T19:32:55.237015Z", - "iopub.status.idle": "2024-09-06T19:32:55.240074Z", - "shell.execute_reply": "2024-09-06T19:32:55.239512Z" + "iopub.execute_input": "2024-09-26T14:46:54.258978Z", + "iopub.status.busy": "2024-09-26T14:46:54.258613Z", + "iopub.status.idle": "2024-09-26T14:46:54.261400Z", + "shell.execute_reply": "2024-09-26T14:46:54.260928Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:55.242072Z", - "iopub.status.busy": "2024-09-06T19:32:55.241898Z", - "iopub.status.idle": "2024-09-06T19:32:57.303999Z", - "shell.execute_reply": "2024-09-06T19:32:57.303194Z" + "iopub.execute_input": "2024-09-26T14:46:54.263212Z", + "iopub.status.busy": "2024-09-26T14:46:54.262873Z", + "iopub.status.idle": "2024-09-26T14:46:56.458045Z", + "shell.execute_reply": "2024-09-26T14:46:56.457352Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:57.307062Z", - "iopub.status.busy": "2024-09-06T19:32:57.306412Z", - "iopub.status.idle": "2024-09-06T19:32:57.318236Z", - "shell.execute_reply": "2024-09-06T19:32:57.317761Z" + "iopub.execute_input": "2024-09-26T14:46:56.460937Z", + "iopub.status.busy": "2024-09-26T14:46:56.459991Z", + "iopub.status.idle": "2024-09-26T14:46:56.471759Z", + "shell.execute_reply": "2024-09-26T14:46:56.471273Z" } }, "outputs": [ @@ -786,10 +786,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:32:57.320219Z", - "iopub.status.busy": "2024-09-06T19:32:57.320039Z", - "iopub.status.idle": "2024-09-06T19:32:57.425487Z", - "shell.execute_reply": "2024-09-06T19:32:57.424961Z" + "iopub.execute_input": "2024-09-26T14:46:56.473438Z", + "iopub.status.busy": "2024-09-26T14:46:56.473240Z", + "iopub.status.idle": "2024-09-26T14:46:56.529027Z", + "shell.execute_reply": "2024-09-26T14:46:56.528533Z" }, "nbsphinx": "hidden" }, @@ -827,7 +827,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index 7c3947e74..81bd9574d 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": "2024-09-06T19:33:00.675758Z", - "iopub.status.busy": "2024-09-06T19:33:00.675584Z", - "iopub.status.idle": "2024-09-06T19:33:03.510616Z", - "shell.execute_reply": "2024-09-06T19:33:03.510057Z" + "iopub.execute_input": "2024-09-26T14:47:00.005766Z", + "iopub.status.busy": "2024-09-26T14:47:00.005598Z", + "iopub.status.idle": "2024-09-26T14:47:03.458146Z", + "shell.execute_reply": "2024-09-26T14:47:03.457580Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:03.513184Z", - "iopub.status.busy": "2024-09-06T19:33:03.512761Z", - "iopub.status.idle": "2024-09-06T19:33:03.516199Z", - "shell.execute_reply": "2024-09-06T19:33:03.515742Z" + "iopub.execute_input": "2024-09-26T14:47:03.460102Z", + "iopub.status.busy": "2024-09-26T14:47:03.459810Z", + "iopub.status.idle": "2024-09-26T14:47:03.463418Z", + "shell.execute_reply": "2024-09-26T14:47:03.462845Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.518261Z", - "iopub.status.busy": "2024-09-06T19:33:03.517871Z", - "iopub.status.idle": "2024-09-06T19:33:03.520905Z", - "shell.execute_reply": "2024-09-06T19:33:03.520432Z" + "iopub.execute_input": "2024-09-26T14:47:03.465142Z", + "iopub.status.busy": "2024-09-26T14:47:03.464800Z", + "iopub.status.idle": "2024-09-26T14:47:03.467949Z", + "shell.execute_reply": "2024-09-26T14:47:03.467483Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.522785Z", - "iopub.status.busy": "2024-09-06T19:33:03.522608Z", - "iopub.status.idle": "2024-09-06T19:33:03.678565Z", - "shell.execute_reply": "2024-09-06T19:33:03.678029Z" + "iopub.execute_input": "2024-09-26T14:47:03.469646Z", + "iopub.status.busy": "2024-09-26T14:47:03.469283Z", + "iopub.status.idle": "2024-09-26T14:47:03.521848Z", + "shell.execute_reply": "2024-09-26T14:47:03.521259Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.680851Z", - "iopub.status.busy": "2024-09-06T19:33:03.680423Z", - "iopub.status.idle": "2024-09-06T19:33:03.684124Z", - "shell.execute_reply": "2024-09-06T19:33:03.683591Z" + "iopub.execute_input": "2024-09-26T14:47:03.523805Z", + "iopub.status.busy": "2024-09-26T14:47:03.523447Z", + "iopub.status.idle": "2024-09-26T14:47:03.527108Z", + "shell.execute_reply": "2024-09-26T14:47:03.526668Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.686150Z", - "iopub.status.busy": "2024-09-06T19:33:03.685759Z", - "iopub.status.idle": "2024-09-06T19:33:03.689186Z", - "shell.execute_reply": "2024-09-06T19:33:03.688640Z" + "iopub.execute_input": "2024-09-26T14:47:03.528762Z", + "iopub.status.busy": "2024-09-26T14:47:03.528492Z", + "iopub.status.idle": "2024-09-26T14:47:03.532073Z", + "shell.execute_reply": "2024-09-26T14:47:03.531625Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'card_payment_fee_charged'}\n" + "Classes: {'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_about_to_expire'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.691223Z", - "iopub.status.busy": "2024-09-06T19:33:03.690802Z", - "iopub.status.idle": "2024-09-06T19:33:03.693946Z", - "shell.execute_reply": "2024-09-06T19:33:03.693394Z" + "iopub.execute_input": "2024-09-26T14:47:03.533776Z", + "iopub.status.busy": "2024-09-26T14:47:03.533438Z", + "iopub.status.idle": "2024-09-26T14:47:03.536702Z", + "shell.execute_reply": "2024-09-26T14:47:03.536252Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.695918Z", - "iopub.status.busy": "2024-09-06T19:33:03.695618Z", - "iopub.status.idle": "2024-09-06T19:33:03.698740Z", - "shell.execute_reply": "2024-09-06T19:33:03.698281Z" + "iopub.execute_input": "2024-09-26T14:47:03.538408Z", + "iopub.status.busy": "2024-09-26T14:47:03.538094Z", + "iopub.status.idle": "2024-09-26T14:47:03.541437Z", + "shell.execute_reply": "2024-09-26T14:47:03.540871Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.700642Z", - "iopub.status.busy": "2024-09-06T19:33:03.700468Z", - "iopub.status.idle": "2024-09-06T19:33:08.790650Z", - "shell.execute_reply": "2024-09-06T19:33:08.789991Z" + "iopub.execute_input": "2024-09-26T14:47:03.543307Z", + "iopub.status.busy": "2024-09-26T14:47:03.542863Z", + "iopub.status.idle": "2024-09-26T14:47:08.488107Z", + "shell.execute_reply": "2024-09-26T14:47:08.487533Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "501ba738bb5947ccaad0e2cd1f842b14", + "model_id": "7bf569b1ec4240fbb7f1457722fe46c9", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31304fdb61a94d1eb88890ad65421b88", + "model_id": "dc4eb1dc64da457a9d83b0bad4f4fd96", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a73d7a796fe45fca51bb3d3b1eb08df", + "model_id": "47dd26560f0f4f14ae1d6235bf187f43", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4b2323ffd9349f1ad2d4d50a0288dc5", + "model_id": "6692a301895241f7894a3bace80aec4a", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "620076f191a74b5c914c7a2b17db4f55", + "model_id": "21332e3c65394cf38141b89a7102833d", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e6b938e7ce354e6ebb9c5105fe3bde01", + "model_id": "37b540a8453d4401b5a798e49297b5a2", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "122704b7d1124989a50bdf83f04c3039", + "model_id": "c550a7da6dee4658a5e958b278220075", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.793264Z", - "iopub.status.busy": "2024-09-06T19:33:08.793080Z", - "iopub.status.idle": "2024-09-06T19:33:08.795949Z", - "shell.execute_reply": "2024-09-06T19:33:08.795369Z" + "iopub.execute_input": "2024-09-26T14:47:08.490505Z", + "iopub.status.busy": "2024-09-26T14:47:08.490089Z", + "iopub.status.idle": "2024-09-26T14:47:08.493151Z", + "shell.execute_reply": "2024-09-26T14:47:08.492644Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.797847Z", - "iopub.status.busy": "2024-09-06T19:33:08.797676Z", - "iopub.status.idle": "2024-09-06T19:33:08.800380Z", - "shell.execute_reply": "2024-09-06T19:33:08.799925Z" + "iopub.execute_input": "2024-09-26T14:47:08.494934Z", + "iopub.status.busy": "2024-09-26T14:47:08.494590Z", + "iopub.status.idle": "2024-09-26T14:47:08.497376Z", + "shell.execute_reply": "2024-09-26T14:47:08.496905Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.802410Z", - "iopub.status.busy": "2024-09-06T19:33:08.802073Z", - "iopub.status.idle": "2024-09-06T19:33:11.565675Z", - "shell.execute_reply": "2024-09-06T19:33:11.564900Z" + "iopub.execute_input": "2024-09-26T14:47:08.499043Z", + "iopub.status.busy": "2024-09-26T14:47:08.498709Z", + "iopub.status.idle": "2024-09-26T14:47:11.411424Z", + "shell.execute_reply": "2024-09-26T14:47:11.410600Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.569102Z", - "iopub.status.busy": "2024-09-06T19:33:11.568193Z", - "iopub.status.idle": "2024-09-06T19:33:11.576067Z", - "shell.execute_reply": "2024-09-06T19:33:11.575576Z" + "iopub.execute_input": "2024-09-26T14:47:11.414407Z", + "iopub.status.busy": "2024-09-26T14:47:11.413552Z", + "iopub.status.idle": "2024-09-26T14:47:11.421686Z", + "shell.execute_reply": "2024-09-26T14:47:11.421111Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.578560Z", - "iopub.status.busy": "2024-09-06T19:33:11.578144Z", - "iopub.status.idle": "2024-09-06T19:33:11.582288Z", - "shell.execute_reply": "2024-09-06T19:33:11.581717Z" + "iopub.execute_input": "2024-09-26T14:47:11.423755Z", + "iopub.status.busy": "2024-09-26T14:47:11.423300Z", + "iopub.status.idle": "2024-09-26T14:47:11.427791Z", + "shell.execute_reply": "2024-09-26T14:47:11.427280Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.584328Z", - "iopub.status.busy": "2024-09-06T19:33:11.583988Z", - "iopub.status.idle": "2024-09-06T19:33:11.587376Z", - "shell.execute_reply": "2024-09-06T19:33:11.586902Z" + "iopub.execute_input": "2024-09-26T14:47:11.429713Z", + "iopub.status.busy": "2024-09-26T14:47:11.429285Z", + "iopub.status.idle": "2024-09-26T14:47:11.432675Z", + "shell.execute_reply": "2024-09-26T14:47:11.432217Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.589547Z", - "iopub.status.busy": "2024-09-06T19:33:11.589216Z", - "iopub.status.idle": "2024-09-06T19:33:11.592104Z", - "shell.execute_reply": "2024-09-06T19:33:11.591660Z" + "iopub.execute_input": "2024-09-26T14:47:11.434490Z", + "iopub.status.busy": "2024-09-26T14:47:11.434156Z", + "iopub.status.idle": "2024-09-26T14:47:11.437228Z", + "shell.execute_reply": "2024-09-26T14:47:11.436765Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.594213Z", - "iopub.status.busy": "2024-09-06T19:33:11.593882Z", - "iopub.status.idle": "2024-09-06T19:33:11.600605Z", - "shell.execute_reply": "2024-09-06T19:33:11.600152Z" + "iopub.execute_input": "2024-09-26T14:47:11.438815Z", + "iopub.status.busy": "2024-09-26T14:47:11.438635Z", + "iopub.status.idle": "2024-09-26T14:47:11.446087Z", + "shell.execute_reply": "2024-09-26T14:47:11.445615Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.602671Z", - "iopub.status.busy": "2024-09-06T19:33:11.602343Z", - "iopub.status.idle": "2024-09-06T19:33:11.828596Z", - "shell.execute_reply": "2024-09-06T19:33:11.828033Z" + "iopub.execute_input": "2024-09-26T14:47:11.447943Z", + "iopub.status.busy": "2024-09-26T14:47:11.447610Z", + "iopub.status.idle": "2024-09-26T14:47:11.721319Z", + "shell.execute_reply": "2024-09-26T14:47:11.720698Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.831240Z", - "iopub.status.busy": "2024-09-06T19:33:11.830841Z", - "iopub.status.idle": "2024-09-06T19:33:12.009186Z", - "shell.execute_reply": "2024-09-06T19:33:12.008615Z" + "iopub.execute_input": "2024-09-26T14:47:11.723702Z", + "iopub.status.busy": "2024-09-26T14:47:11.723282Z", + "iopub.status.idle": "2024-09-26T14:47:11.904646Z", + "shell.execute_reply": "2024-09-26T14:47:11.904102Z" }, "scrolled": true }, @@ -1073,10 +1073,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:12.011827Z", - "iopub.status.busy": "2024-09-06T19:33:12.011435Z", - "iopub.status.idle": "2024-09-06T19:33:12.015256Z", - "shell.execute_reply": "2024-09-06T19:33:12.014755Z" + "iopub.execute_input": "2024-09-26T14:47:11.906952Z", + "iopub.status.busy": "2024-09-26T14:47:11.906556Z", + "iopub.status.idle": "2024-09-26T14:47:11.910954Z", + "shell.execute_reply": "2024-09-26T14:47:11.910437Z" }, "nbsphinx": "hidden" }, @@ -1115,12 +1115,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0459048c4f99420aa195e714b5d9a0fd": { + "03473efde97f4db7aeb4bf94a8dcb6ac": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "06bdd0de78584b4d8c963a129ebf2a90": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1173,56 +1191,76 @@ "width": null } }, - "0828f51c6fc84a0da65b7ec09b69d580": { + "076de96beb79463ab6936bf1bcf942f5": { "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_60ed3679f364477387473b90089b8273", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9c60afb1733442418ab367430bbb0d68", - "tabbable": null, - "tooltip": null, - "value": 391.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "08df668bdad845f9a6fca994bd1ecd5d": { - "model_module": "@jupyter-widgets/controls", + "0988aba1120e44e194591113d57b63d3": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_66ad826330ec48dcbbb58f5f351a1112", - "placeholder": "​", - "style": "IPY_MODEL_8adb666a2a6e4976a7051e3074b29507", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "094ab3c4cb4649dbb35cf60d64c57d3d": { + "09d4dbd7007b4cfda79c92844565bfd3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1275,7 +1313,7 @@ "width": null } }, - "10da9ba1022e498b8f5f3f98c7898223": { + "0a9bbe4ae4a7460aa182a6b390e2126a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1328,49 +1366,92 @@ "width": null } }, - "122704b7d1124989a50bdf83f04c3039": { + "0b20d7b224384162a625ab6b3d0f9b75": { "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_f6577867a1c041f490b073388151f1be", - "IPY_MODEL_3e00fec335784d3d8f67aef8d5205c3a", - "IPY_MODEL_9692da0a0ad949f39b93867a9112ab58" - ], - "layout": "IPY_MODEL_c876ccec48d84a04bb874ff6b48f8030", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_159894c2bd95445e89e4038c12c6bb9f", + "placeholder": "​", + "style": "IPY_MODEL_cf82eb1ddad343378d886f2d760e8e19", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 95.5MB/s]" } }, - "154d34bbb18c4e6bb7471d21431c8407": { + "0bfe9f713e2d4b6ba3ef62402a269ffb": { "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_ecf47058d5624d17a0cd5595cc9a9ea1", + "placeholder": "​", + "style": "IPY_MODEL_5f805e6909fa4676b560df990b1225b3", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "12a2c923802042bbaf7afcb489317fc2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1fc253a07301484fbf34df123112adf2", + "placeholder": "​", + "style": "IPY_MODEL_2745511db571407b909eadcf1041fbbb", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 33.8MB/s]" + } + }, + "13323479403a4c6ba63c87932eb6b7d1": { + "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": "" } }, - "18fbcd7e9e6f47f89dbd1bf8da0aac40": { + "159894c2bd95445e89e4038c12c6bb9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1423,7 +1504,7 @@ "width": null } }, - "1b4847f4ce0741c391f613e88b131aaa": { + "16a4915303e24ee999326b95f314a277": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1476,67 +1557,60 @@ "width": null } }, - "27cccac5127445d09232f53a08657063": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "290f1d04116240fbaa62e5ec4b1a24a2": { - "model_module": "@jupyter-widgets/controls", + "17d77342577f4828b221978f61b1cca1": { + "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": "" - } - }, - "2b4d16d0c52d49b4b207e9e5d8450870": { - "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_0459048c4f99420aa195e714b5d9a0fd", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_49e660a0942a4523b89e94f5b3f10d5e", - "tabbable": null, - "tooltip": null, - "value": 466062.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "2fa841989e734ca59ab3392a1c472375": { + "18587e575408498d91cc167ce94971e8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1589,72 +1663,60 @@ "width": null } }, - "31304fdb61a94d1eb88890ad65421b88": { - "model_module": "@jupyter-widgets/controls", + "1b45076c459847edb676c5da860e7c63": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_87bd24bf12c243a6b9cca97e02ea6f70", - "IPY_MODEL_ea5aad74db984381a9502d15f7877dc9", - "IPY_MODEL_b917898a95b64706aca98aba5a2b9969" - ], - "layout": "IPY_MODEL_748657e3cb9543698e92e614f2b8352c", - "tabbable": null, - "tooltip": null - } - }, - "3248bde1a32e421da1664cd2d4d3419e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b8283fed28004000b84e2f53babadac1", - "placeholder": "​", - "style": "IPY_MODEL_8d94e369761f41d087d990935dbe60c2", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" - } - }, - "34ddf006f73a4753b211a5999ec0d671": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "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 } }, - "390080de929440ada6a97f2e0d2dc60f": { + "1c9880baaa514557b939f1bc383fd6dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1672,33 +1734,7 @@ "text_color": null } }, - "3e00fec335784d3d8f67aef8d5205c3a": { - "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_908e91876b774d93a02042ae9035283d", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_290f1d04116240fbaa62e5ec4b1a24a2", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "4071e96fc1124ad3bff4e7fe0f035295": { + "1f782846199a4cd886bc9a2d8b267db5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1751,57 +1787,7 @@ "width": null } }, - "470a184131ad4f9789eb904333469e81": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "49e660a0942a4523b89e94f5b3f10d5e": { - "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": "" - } - }, - "4bb99f8cedeb4182a94727c634341364": { - "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": "" - } - }, - "4cb28f8d4c2846bba02492de8371ef86": { + "1fc253a07301484fbf34df123112adf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1854,30 +1840,7 @@ "width": null } }, - "4dd3d95d46d846b98d4c8e1fca170cc1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f7f7d73c85274703989c1a758316f306", - "placeholder": "​", - "style": "IPY_MODEL_96ad5243a28540c1bbd13701050cd8c8", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 44.9MB/s]" - } - }, - "501ba738bb5947ccaad0e2cd1f842b14": { + "21332e3c65394cf38141b89a7102833d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1892,63 +1855,50 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_5b8638296ff64c2abc68c70b1b8b7469", - "IPY_MODEL_0828f51c6fc84a0da65b7ec09b69d580", - "IPY_MODEL_dc79b458bbac454fbab119272509e252" + "IPY_MODEL_723beeec471a4b999f642afe6f412d4f", + "IPY_MODEL_6a1338550c7e47f2ace913c6db6703dc", + "IPY_MODEL_60390240aff543d7947dd2f2f3272bde" ], - "layout": "IPY_MODEL_094ab3c4cb4649dbb35cf60d64c57d3d", + "layout": "IPY_MODEL_1b45076c459847edb676c5da860e7c63", "tabbable": null, "tooltip": null } }, - "5a73d7a796fe45fca51bb3d3b1eb08df": { + "2745511db571407b909eadcf1041fbbb": { "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_08df668bdad845f9a6fca994bd1ecd5d", - "IPY_MODEL_88b14d6b576d4c358576ada8914fc9ae", - "IPY_MODEL_efcccd1f66e4459cb1a7709eadb26866" - ], - "layout": "IPY_MODEL_18fbcd7e9e6f47f89dbd1bf8da0aac40", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5b8638296ff64c2abc68c70b1b8b7469": { + "27f46a8464f6451d9729e0085ec07516": { "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_dd603ce908534d83a9e5812536cbaecf", - "placeholder": "​", - "style": "IPY_MODEL_87f402b0764744998c823bf8713ee0ae", - "tabbable": null, - "tooltip": null, - "value": ".gitattributes: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "5bed445f56a545b89b799f73d2462bd9": { + "2b2f96f6470846f1bd8aae7cb5dbeed7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1964,137 +1914,48 @@ "description_width": "" } }, - "5f624de673e9405cb01619e550cd02b5": { - "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 - } - }, - "60ed3679f364477387473b90089b8273": { - "model_module": "@jupyter-widgets/base", + "2b379296834846fa97be92e4b2d6df99": { + "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 } }, - "620076f191a74b5c914c7a2b17db4f55": { + "2b51658eec434f6c8284bd799690104b": { "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_3248bde1a32e421da1664cd2d4d3419e", - "IPY_MODEL_2b4d16d0c52d49b4b207e9e5d8450870", - "IPY_MODEL_4dd3d95d46d846b98d4c8e1fca170cc1" - ], - "layout": "IPY_MODEL_4071e96fc1124ad3bff4e7fe0f035295", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e06dfc4b5e33464b8b271badc88425c4", + "placeholder": "​", + "style": "IPY_MODEL_1c9880baaa514557b939f1bc383fd6dd", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "vocab.txt: 100%" } }, - "626cdb8c3d374c168811bd920a5a68f8": { + "2b64f899a53543fb8a6355d358ad40dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2147,46 +2008,57 @@ "width": null } }, - "62cc1ededdbc4ebaa9a9455fc402d06e": { + "3127352d799744d391cc01252a89de9a": { "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_c1eada512714499a9689453bf4dd17bb", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_27f46a8464f6451d9729e0085ec07516", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "65182ed5c6764915b44ed26f3452c6e8": { + "37b540a8453d4401b5a798e49297b5a2": { "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_93a4c1dfcfc44589926437f1ffdd3a85", - "placeholder": "​", - "style": "IPY_MODEL_c4c2ce7cae784b2093ba53e3609cc2c9", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f85452b3a7ee4bd8b96746e8f9cbd2a8", + "IPY_MODEL_6431ceb1e80c48e09225d44e2f40b86d", + "IPY_MODEL_a8240a8e7877465086dc27694c472f92" + ], + "layout": "IPY_MODEL_604c0d5a866c4704a89f806366a61376", "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" + "tooltip": null } }, - "66ad826330ec48dcbbb58f5f351a1112": { + "3d76c17d5f61437bbb3f8fdefd0c78fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2222,42 +2094,24 @@ "height": null, "justify_content": null, "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "673761d4701d4ee5aaabc0e22e4ec6cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6b23b979b2f7419a924a8685e13b11a7": { + "3e46cd86409a4a129a6d5d33c5d96e40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2310,25 +2164,7 @@ "width": null } }, - "73e88da4acc94040992fd88d0a0d19ed": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "748657e3cb9543698e92e614f2b8352c": { + "40e898ed62284e21bf36ba199efb74ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2381,7 +2217,33 @@ "width": null } }, - "766a43c7b32e464cb876b478a34ad457": { + "4493cbda5ade4530a4f0dad6cc558cfa": { + "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_3d76c17d5f61437bbb3f8fdefd0c78fa", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_13323479403a4c6ba63c87932eb6b7d1", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "45bfcc5c5c3e4a10b411f71fb3237d72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2397,33 +2259,54 @@ "description_width": "" } }, - "84060f74615349bd9e7f70b839d37c3e": { + "46c6e93fc99f4795bfc2b82c11b1a4d6": { "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_6b23b979b2f7419a924a8685e13b11a7", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_766a43c7b32e464cb876b478a34ad457", + "layout": "IPY_MODEL_1f782846199a4cd886bc9a2d8b267db5", + "placeholder": "​", + "style": "IPY_MODEL_f350410541a947db9efae73d4e6d000d", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 665/665 [00:00<00:00, 129kB/s]" + } + }, + "47dd26560f0f4f14ae1d6235bf187f43": { + "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_adcd6fe3722a404cbb4578c43dfb8622", + "IPY_MODEL_6ef5403b4f904ab5bad25124da8132c1", + "IPY_MODEL_46c6e93fc99f4795bfc2b82c11b1a4d6" + ], + "layout": "IPY_MODEL_fb13c0f13bd34a04a46ba3462d87a3cb", + "tabbable": null, + "tooltip": null } }, - "846cc1a5fbd9438da4609b439141f308": { + "4e78d34c35c64a8ba9e42340cface541": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2476,30 +2359,25 @@ "width": null } }, - "87bd24bf12c243a6b9cca97e02ea6f70": { + "5d9fbb50ea574e9f80a04d65984ec3fb": { "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_fa4036e56d43472283e556f828cf84fd", - "placeholder": "​", - "style": "IPY_MODEL_470a184131ad4f9789eb904333469e81", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "87f402b0764744998c823bf8713ee0ae": { + "5f805e6909fa4676b560df990b1225b3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2517,33 +2395,30 @@ "text_color": null } }, - "88b14d6b576d4c358576ada8914fc9ae": { + "60390240aff543d7947dd2f2f3272bde": { "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_c634f1241a9e40659957b6a8dd57b66b", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4bb99f8cedeb4182a94727c634341364", + "layout": "IPY_MODEL_7d0ebaac4f0b43a498272ea2a11b15a3", + "placeholder": "​", + "style": "IPY_MODEL_2b379296834846fa97be92e4b2d6df99", "tabbable": null, "tooltip": null, - "value": 665.0 + "value": " 466k/466k [00:00<00:00, 14.0MB/s]" } }, - "89c1af10a3bf46378b2d5ad1570f4844": { + "604c0d5a866c4704a89f806366a61376": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2596,7 +2471,7 @@ "width": null } }, - "8adb666a2a6e4976a7051e3074b29507": { + "617bb9dddcf14ecb9bae9c187156bb6c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2614,7 +2489,57 @@ "text_color": null } }, - "8d94e369761f41d087d990935dbe60c2": { + "6431ceb1e80c48e09225d44e2f40b86d": { + "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_0988aba1120e44e194591113d57b63d3", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8944d62a93fe49f991ff8bacfeac322c", + "tabbable": null, + "tooltip": null, + "value": 48.0 + } + }, + "6692a301895241f7894a3bace80aec4a": { + "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_0bfe9f713e2d4b6ba3ef62402a269ffb", + "IPY_MODEL_4493cbda5ade4530a4f0dad6cc558cfa", + "IPY_MODEL_0b20d7b224384162a625ab6b3d0f9b75" + ], + "layout": "IPY_MODEL_18587e575408498d91cc167ce94971e8", + "tabbable": null, + "tooltip": null + } + }, + "69666c8921454e55aebdd3cb6ea39a73": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2632,60 +2557,77 @@ "text_color": null } }, - "908e91876b774d93a02042ae9035283d": { - "model_module": "@jupyter-widgets/base", + "6a1338550c7e47f2ace913c6db6703dc": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_06bdd0de78584b4d8c963a129ebf2a90", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_076de96beb79463ab6936bf1bcf942f5", + "tabbable": null, + "tooltip": null, + "value": 466062.0 + } + }, + "6b9cbd8a34764f13b850393a56c2c764": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6ef5403b4f904ab5bad25124da8132c1": { + "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_77b2da3b157a4e658101eddffc7a0c02", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2b2f96f6470846f1bd8aae7cb5dbeed7", + "tabbable": null, + "tooltip": null, + "value": 665.0 } }, - "93a4c1dfcfc44589926437f1ffdd3a85": { + "70176e26d6ca48bda5d33005335369ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2738,7 +2680,7 @@ "width": null } }, - "9692da0a0ad949f39b93867a9112ab58": { + "723beeec471a4b999f642afe6f412d4f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2753,33 +2695,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d4493c8ca6d74012b8cda7556ffcfcbb", + "layout": "IPY_MODEL_0a9bbe4ae4a7460aa182a6b390e2126a", "placeholder": "​", - "style": "IPY_MODEL_154d34bbb18c4e6bb7471d21431c8407", + "style": "IPY_MODEL_617bb9dddcf14ecb9bae9c187156bb6c", "tabbable": null, "tooltip": null, - "value": " 232k/232k [00:00<00:00, 3.64MB/s]" - } - }, - "96ad5243a28540c1bbd13701050cd8c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "tokenizer.json: 100%" } }, - "98a1dc889ed543ddb76c46e918f80a38": { + "723f6f685bea42f196189855c383ef77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2794,15 +2718,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2fa841989e734ca59ab3392a1c472375", + "layout": "IPY_MODEL_7b295c74e39947a6b06414e1b23aef24", "placeholder": "​", - "style": "IPY_MODEL_73e88da4acc94040992fd88d0a0d19ed", + "style": "IPY_MODEL_6b9cbd8a34764f13b850393a56c2c764", "tabbable": null, "tooltip": null, - "value": "tokenizer_config.json: 100%" + "value": " 2.21k/2.21k [00:00<00:00, 409kB/s]" + } + }, + "77b2da3b157a4e658101eddffc7a0c02": { + "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 } }, - "99072ca746ea485cbb170400eb0e5a45": { + "7b295c74e39947a6b06414e1b23aef24": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2855,23 +2832,31 @@ "width": null } }, - "9c60afb1733442418ab367430bbb0d68": { + "7bf569b1ec4240fbb7f1457722fe46c9": { "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_f585c8f15e5c4595a88ccd3bd0205013", + "IPY_MODEL_9116b550551046e580f470834f6b5b3a", + "IPY_MODEL_b46d0225e69b453dac54123c7dfe9aa9" + ], + "layout": "IPY_MODEL_16a4915303e24ee999326b95f314a277", + "tabbable": null, + "tooltip": null } }, - "aa7d45a4b72346af98fb57bd52e6b237": { + "7d0ebaac4f0b43a498272ea2a11b15a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2924,67 +2909,134 @@ "width": null } }, - "ae26881588fc4f9695b5cbd0549eb30a": { + "8944d62a93fe49f991ff8bacfeac322c": { "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": "" } }, - "af41f929d6ed42e8b6fa4fd762ea4ebe": { + "8ad231daa0c44bdbabf3c3cc6a0548db": { "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_3e46cd86409a4a129a6d5d33c5d96e40", + "placeholder": "​", + "style": "IPY_MODEL_69666c8921454e55aebdd3cb6ea39a73", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" + } + }, + "9116b550551046e580f470834f6b5b3a": { + "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_e5f98261e33e4e68983283a794b3ce3e", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9c07f5c15bae4ce8b5469d47f4fedca6", + "tabbable": null, + "tooltip": null, + "value": 391.0 + } + }, + "9c07f5c15bae4ce8b5469d47f4fedca6": { + "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": "" } }, - "b4b2323ffd9349f1ad2d4d50a0288dc5": { + "a8240a8e7877465086dc27694c472f92": { "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_65182ed5c6764915b44ed26f3452c6e8", - "IPY_MODEL_84060f74615349bd9e7f70b839d37c3e", - "IPY_MODEL_b76df53d2353433cb2fea0cde0c2d1dd" - ], - "layout": "IPY_MODEL_626cdb8c3d374c168811bd920a5a68f8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_70176e26d6ca48bda5d33005335369ce", + "placeholder": "​", + "style": "IPY_MODEL_f8e1af21c2314931b28a2ffdccd38cc1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.68kB/s]" + } + }, + "adcd6fe3722a404cbb4578c43dfb8622": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_bfef1d0d61ca4f1f8e1818ed08702bea", + "placeholder": "​", + "style": "IPY_MODEL_ec66181f622246e6b126cf01ee0e0c1f", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" } }, - "b76df53d2353433cb2fea0cde0c2d1dd": { + "b46d0225e69b453dac54123c7dfe9aa9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2999,15 +3051,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4cb28f8d4c2846bba02492de8371ef86", + "layout": "IPY_MODEL_4e78d34c35c64a8ba9e42340cface541", "placeholder": "​", - "style": "IPY_MODEL_673761d4701d4ee5aaabc0e22e4ec6cb", + "style": "IPY_MODEL_5d9fbb50ea574e9f80a04d65984ec3fb", "tabbable": null, "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 206MB/s]" + "value": " 391/391 [00:00<00:00, 62.9kB/s]" + } + }, + "bfef1d0d61ca4f1f8e1818ed08702bea": { + "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 } }, - "b8283fed28004000b84e2f53babadac1": { + "c1eada512714499a9689453bf4dd17bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3060,71 +3165,99 @@ "width": null } }, - "b917898a95b64706aca98aba5a2b9969": { + "c550a7da6dee4658a5e958b278220075": { + "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_2b51658eec434f6c8284bd799690104b", + "IPY_MODEL_d117d190931044b78c7d6506f9a291f8", + "IPY_MODEL_12a2c923802042bbaf7afcb489317fc2" + ], + "layout": "IPY_MODEL_2b64f899a53543fb8a6355d358ad40dc", + "tabbable": null, + "tooltip": null + } + }, + "cf82eb1ddad343378d886f2d760e8e19": { "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_846cc1a5fbd9438da4609b439141f308", - "placeholder": "​", - "style": "IPY_MODEL_af41f929d6ed42e8b6fa4fd762ea4ebe", - "tabbable": null, - "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 314kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bc14afac06eb4c26bf5c7c100334328e": { + "d117d190931044b78c7d6506f9a291f8": { "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_10da9ba1022e498b8f5f3f98c7898223", - "placeholder": "​", - "style": "IPY_MODEL_34ddf006f73a4753b211a5999ec0d671", + "layout": "IPY_MODEL_40e898ed62284e21bf36ba199efb74ca", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_45bfcc5c5c3e4a10b411f71fb3237d72", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.65kB/s]" + "value": 231508.0 } }, - "c4c2ce7cae784b2093ba53e3609cc2c9": { + "dc4eb1dc64da457a9d83b0bad4f4fd96": { "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_8ad231daa0c44bdbabf3c3cc6a0548db", + "IPY_MODEL_3127352d799744d391cc01252a89de9a", + "IPY_MODEL_723f6f685bea42f196189855c383ef77" + ], + "layout": "IPY_MODEL_e996d9e989544a01a9db1b3ba3e4bdb6", + "tabbable": null, + "tooltip": null } }, - "c634f1241a9e40659957b6a8dd57b66b": { + "e06dfc4b5e33464b8b271badc88425c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3177,7 +3310,7 @@ "width": null } }, - "c876ccec48d84a04bb874ff6b48f8030": { + "e5f98261e33e4e68983283a794b3ce3e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3230,7 +3363,7 @@ "width": null } }, - "d4493c8ca6d74012b8cda7556ffcfcbb": { + "e996d9e989544a01a9db1b3ba3e4bdb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3283,30 +3416,43 @@ "width": null } }, - "dc79b458bbac454fbab119272509e252": { + "ec66181f622246e6b126cf01ee0e0c1f": { "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_99072ca746ea485cbb170400eb0e5a45", - "placeholder": "​", - "style": "IPY_MODEL_ae26881588fc4f9695b5cbd0549eb30a", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 56.2kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "eca51dd4d6224552b6d374e90dd7f129": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "dd603ce908534d83a9e5812536cbaecf": { + "ecf47058d5624d17a0cd5595cc9a9ea1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3359,57 +3505,25 @@ "width": null } }, - "e6b938e7ce354e6ebb9c5105fe3bde01": { - "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_98a1dc889ed543ddb76c46e918f80a38", - "IPY_MODEL_fbbd836ae0ab4f63bed278c2565cd3f1", - "IPY_MODEL_bc14afac06eb4c26bf5c7c100334328e" - ], - "layout": "IPY_MODEL_aa7d45a4b72346af98fb57bd52e6b237", - "tabbable": null, - "tooltip": null - } - }, - "ea5aad74db984381a9502d15f7877dc9": { + "f350410541a947db9efae73d4e6d000d": { "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_5f624de673e9405cb01619e550cd02b5", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_62cc1ededdbc4ebaa9a9455fc402d06e", - "tabbable": null, - "tooltip": null, - "value": 2211.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "efcccd1f66e4459cb1a7709eadb26866": { + "f585c8f15e5c4595a88ccd3bd0205013": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3424,15 +3538,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f94845de837b4883b0f99fb9f3b7ead2", + "layout": "IPY_MODEL_17d77342577f4828b221978f61b1cca1", "placeholder": "​", - "style": "IPY_MODEL_27cccac5127445d09232f53a08657063", + "style": "IPY_MODEL_03473efde97f4db7aeb4bf94a8dcb6ac", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 124kB/s]" + "value": ".gitattributes: 100%" } }, - "f6577867a1c041f490b073388151f1be": { + "f85452b3a7ee4bd8b96746e8f9cbd2a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3447,121 +3561,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_89c1af10a3bf46378b2d5ad1570f4844", + "layout": "IPY_MODEL_09d4dbd7007b4cfda79c92844565bfd3", "placeholder": "​", - "style": "IPY_MODEL_390080de929440ada6a97f2e0d2dc60f", + "style": "IPY_MODEL_eca51dd4d6224552b6d374e90dd7f129", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" - } - }, - "f7f7d73c85274703989c1a758316f306": { - "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": "tokenizer_config.json: 100%" } }, - "f94845de837b4883b0f99fb9f3b7ead2": { - "model_module": "@jupyter-widgets/base", + "f8e1af21c2314931b28a2ffdccd38cc1": { + "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 } }, - "fa4036e56d43472283e556f828cf84fd": { + "fb13c0f13bd34a04a46ba3462d87a3cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3613,32 +3639,6 @@ "visibility": null, "width": null } - }, - "fbbd836ae0ab4f63bed278c2565cd3f1": { - "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_1b4847f4ce0741c391f613e88b131aaa", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5bed445f56a545b89b799f73d2462bd9", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index 29bf50217..25d6d5a74 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": "2024-09-06T19:33:15.412497Z", - "iopub.status.busy": "2024-09-06T19:33:15.412315Z", - "iopub.status.idle": "2024-09-06T19:33:20.744505Z", - "shell.execute_reply": "2024-09-06T19:33:20.743930Z" + "iopub.execute_input": "2024-09-26T14:47:15.535813Z", + "iopub.status.busy": "2024-09-26T14:47:15.535636Z", + "iopub.status.idle": "2024-09-26T14:47:21.234346Z", + "shell.execute_reply": "2024-09-26T14:47:21.233674Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:20.747320Z", - "iopub.status.busy": "2024-09-06T19:33:20.746730Z", - "iopub.status.idle": "2024-09-06T19:33:20.750172Z", - "shell.execute_reply": "2024-09-06T19:33:20.749624Z" + "iopub.execute_input": "2024-09-26T14:47:21.236766Z", + "iopub.status.busy": "2024-09-26T14:47:21.236377Z", + "iopub.status.idle": "2024-09-26T14:47:21.239643Z", + "shell.execute_reply": "2024-09-26T14:47:21.239185Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:20.752397Z", - "iopub.status.busy": "2024-09-06T19:33:20.751947Z", - "iopub.status.idle": "2024-09-06T19:33:20.756917Z", - "shell.execute_reply": "2024-09-06T19:33:20.756445Z" + "iopub.execute_input": "2024-09-26T14:47:21.241324Z", + "iopub.status.busy": "2024-09-26T14:47:21.240999Z", + "iopub.status.idle": "2024-09-26T14:47:21.245772Z", + "shell.execute_reply": "2024-09-26T14:47:21.245316Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:20.758862Z", - "iopub.status.busy": "2024-09-06T19:33:20.758684Z", - "iopub.status.idle": "2024-09-06T19:33:22.662809Z", - "shell.execute_reply": "2024-09-06T19:33:22.662142Z" + "iopub.execute_input": "2024-09-26T14:47:21.247572Z", + "iopub.status.busy": "2024-09-26T14:47:21.247248Z", + "iopub.status.idle": "2024-09-26T14:47:23.090757Z", + "shell.execute_reply": "2024-09-26T14:47:23.089916Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.665411Z", - "iopub.status.busy": "2024-09-06T19:33:22.665209Z", - "iopub.status.idle": "2024-09-06T19:33:22.675958Z", - "shell.execute_reply": "2024-09-06T19:33:22.675514Z" + "iopub.execute_input": "2024-09-26T14:47:23.093045Z", + "iopub.status.busy": "2024-09-26T14:47:23.092832Z", + "iopub.status.idle": "2024-09-26T14:47:23.103750Z", + "shell.execute_reply": "2024-09-26T14:47:23.103275Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.677986Z", - "iopub.status.busy": "2024-09-06T19:33:22.677801Z", - "iopub.status.idle": "2024-09-06T19:33:22.684956Z", - "shell.execute_reply": "2024-09-06T19:33:22.684474Z" + "iopub.execute_input": "2024-09-26T14:47:23.105636Z", + "iopub.status.busy": "2024-09-26T14:47:23.105213Z", + "iopub.status.idle": "2024-09-26T14:47:23.110875Z", + "shell.execute_reply": "2024-09-26T14:47:23.110416Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.686790Z", - "iopub.status.busy": "2024-09-06T19:33:22.686606Z", - "iopub.status.idle": "2024-09-06T19:33:23.132191Z", - "shell.execute_reply": "2024-09-06T19:33:23.131660Z" + "iopub.execute_input": "2024-09-26T14:47:23.112413Z", + "iopub.status.busy": "2024-09-26T14:47:23.112235Z", + "iopub.status.idle": "2024-09-26T14:47:23.593390Z", + "shell.execute_reply": "2024-09-26T14:47:23.592868Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:23.134446Z", - "iopub.status.busy": "2024-09-06T19:33:23.134077Z", - "iopub.status.idle": "2024-09-06T19:33:24.169658Z", - "shell.execute_reply": "2024-09-06T19:33:24.169048Z" + "iopub.execute_input": "2024-09-26T14:47:23.595287Z", + "iopub.status.busy": "2024-09-26T14:47:23.594919Z", + "iopub.status.idle": "2024-09-26T14:47:24.771320Z", + "shell.execute_reply": "2024-09-26T14:47:24.770797Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.172059Z", - "iopub.status.busy": "2024-09-06T19:33:24.171874Z", - "iopub.status.idle": "2024-09-06T19:33:24.191001Z", - "shell.execute_reply": "2024-09-06T19:33:24.190537Z" + "iopub.execute_input": "2024-09-26T14:47:24.773502Z", + "iopub.status.busy": "2024-09-26T14:47:24.773132Z", + "iopub.status.idle": "2024-09-26T14:47:24.791888Z", + "shell.execute_reply": "2024-09-26T14:47:24.791419Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.193080Z", - "iopub.status.busy": "2024-09-06T19:33:24.192898Z", - "iopub.status.idle": "2024-09-06T19:33:24.196091Z", - "shell.execute_reply": "2024-09-06T19:33:24.195633Z" + "iopub.execute_input": "2024-09-26T14:47:24.793633Z", + "iopub.status.busy": "2024-09-26T14:47:24.793274Z", + "iopub.status.idle": "2024-09-26T14:47:24.796512Z", + "shell.execute_reply": "2024-09-26T14:47:24.796062Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.198156Z", - "iopub.status.busy": "2024-09-06T19:33:24.197822Z", - "iopub.status.idle": "2024-09-06T19:33:38.175563Z", - "shell.execute_reply": "2024-09-06T19:33:38.174995Z" + "iopub.execute_input": "2024-09-26T14:47:24.798106Z", + "iopub.status.busy": "2024-09-26T14:47:24.797929Z", + "iopub.status.idle": "2024-09-26T14:47:39.925585Z", + "shell.execute_reply": "2024-09-26T14:47:39.924902Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.178313Z", - "iopub.status.busy": "2024-09-06T19:33:38.177918Z", - "iopub.status.idle": "2024-09-06T19:33:38.181776Z", - "shell.execute_reply": "2024-09-06T19:33:38.181209Z" + "iopub.execute_input": "2024-09-26T14:47:39.927963Z", + "iopub.status.busy": "2024-09-26T14:47:39.927564Z", + "iopub.status.idle": "2024-09-26T14:47:39.931572Z", + "shell.execute_reply": "2024-09-26T14:47:39.931070Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.183755Z", - "iopub.status.busy": "2024-09-06T19:33:38.183579Z", - "iopub.status.idle": "2024-09-06T19:33:38.879592Z", - "shell.execute_reply": "2024-09-06T19:33:38.878973Z" + "iopub.execute_input": "2024-09-26T14:47:39.933402Z", + "iopub.status.busy": "2024-09-26T14:47:39.933168Z", + "iopub.status.idle": "2024-09-26T14:47:40.683130Z", + "shell.execute_reply": "2024-09-26T14:47:40.682533Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.882730Z", - "iopub.status.busy": "2024-09-06T19:33:38.882295Z", - "iopub.status.idle": "2024-09-06T19:33:38.887349Z", - "shell.execute_reply": "2024-09-06T19:33:38.886834Z" + "iopub.execute_input": "2024-09-26T14:47:40.685556Z", + "iopub.status.busy": "2024-09-26T14:47:40.684983Z", + "iopub.status.idle": "2024-09-26T14:47:40.690268Z", + "shell.execute_reply": "2024-09-26T14:47:40.689730Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.889963Z", - "iopub.status.busy": "2024-09-06T19:33:38.889560Z", - "iopub.status.idle": "2024-09-06T19:33:38.996371Z", - "shell.execute_reply": "2024-09-06T19:33:38.995754Z" + "iopub.execute_input": "2024-09-26T14:47:40.693275Z", + "iopub.status.busy": "2024-09-26T14:47:40.692350Z", + "iopub.status.idle": "2024-09-26T14:47:40.818191Z", + "shell.execute_reply": "2024-09-26T14:47:40.817594Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.998935Z", - "iopub.status.busy": "2024-09-06T19:33:38.998516Z", - "iopub.status.idle": "2024-09-06T19:33:39.011487Z", - "shell.execute_reply": "2024-09-06T19:33:39.010948Z" + "iopub.execute_input": "2024-09-26T14:47:40.820305Z", + "iopub.status.busy": "2024-09-26T14:47:40.819910Z", + "iopub.status.idle": "2024-09-26T14:47:40.832452Z", + "shell.execute_reply": "2024-09-26T14:47:40.831961Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.013725Z", - "iopub.status.busy": "2024-09-06T19:33:39.013368Z", - "iopub.status.idle": "2024-09-06T19:33:39.021505Z", - "shell.execute_reply": "2024-09-06T19:33:39.020914Z" + "iopub.execute_input": "2024-09-26T14:47:40.834307Z", + "iopub.status.busy": "2024-09-26T14:47:40.833985Z", + "iopub.status.idle": "2024-09-26T14:47:40.842566Z", + "shell.execute_reply": "2024-09-26T14:47:40.842095Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.023705Z", - "iopub.status.busy": "2024-09-06T19:33:39.023358Z", - "iopub.status.idle": "2024-09-06T19:33:39.027633Z", - "shell.execute_reply": "2024-09-06T19:33:39.027085Z" + "iopub.execute_input": "2024-09-26T14:47:40.844168Z", + "iopub.status.busy": "2024-09-26T14:47:40.843976Z", + "iopub.status.idle": "2024-09-26T14:47:40.848543Z", + "shell.execute_reply": "2024-09-26T14:47:40.847986Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.029757Z", - "iopub.status.busy": "2024-09-06T19:33:39.029380Z", - "iopub.status.idle": "2024-09-06T19:33:39.035357Z", - "shell.execute_reply": "2024-09-06T19:33:39.034867Z" + "iopub.execute_input": "2024-09-26T14:47:40.850235Z", + "iopub.status.busy": "2024-09-26T14:47:40.850049Z", + "iopub.status.idle": "2024-09-26T14:47:40.856001Z", + "shell.execute_reply": "2024-09-26T14:47:40.855443Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.037583Z", - "iopub.status.busy": "2024-09-06T19:33:39.037235Z", - "iopub.status.idle": "2024-09-06T19:33:39.148961Z", - "shell.execute_reply": "2024-09-06T19:33:39.148428Z" + "iopub.execute_input": "2024-09-26T14:47:40.857876Z", + "iopub.status.busy": "2024-09-26T14:47:40.857600Z", + "iopub.status.idle": "2024-09-26T14:47:40.971332Z", + "shell.execute_reply": "2024-09-26T14:47:40.970730Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.151080Z", - "iopub.status.busy": "2024-09-06T19:33:39.150801Z", - "iopub.status.idle": "2024-09-06T19:33:39.254384Z", - "shell.execute_reply": "2024-09-06T19:33:39.253890Z" + "iopub.execute_input": "2024-09-26T14:47:40.973220Z", + "iopub.status.busy": "2024-09-26T14:47:40.972870Z", + "iopub.status.idle": "2024-09-26T14:47:41.080277Z", + "shell.execute_reply": "2024-09-26T14:47:41.079768Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.256524Z", - "iopub.status.busy": "2024-09-06T19:33:39.256169Z", - "iopub.status.idle": "2024-09-06T19:33:39.357567Z", - "shell.execute_reply": "2024-09-06T19:33:39.356999Z" + "iopub.execute_input": "2024-09-26T14:47:41.082132Z", + "iopub.status.busy": "2024-09-26T14:47:41.081755Z", + "iopub.status.idle": "2024-09-26T14:47:41.187047Z", + "shell.execute_reply": "2024-09-26T14:47:41.186551Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.359754Z", - "iopub.status.busy": "2024-09-06T19:33:39.359388Z", - "iopub.status.idle": "2024-09-06T19:33:39.459179Z", - "shell.execute_reply": "2024-09-06T19:33:39.458626Z" + "iopub.execute_input": "2024-09-26T14:47:41.188688Z", + "iopub.status.busy": "2024-09-26T14:47:41.188508Z", + "iopub.status.idle": "2024-09-26T14:47:41.292182Z", + "shell.execute_reply": "2024-09-26T14:47:41.291705Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.461397Z", - "iopub.status.busy": "2024-09-06T19:33:39.461068Z", - "iopub.status.idle": "2024-09-06T19:33:39.464273Z", - "shell.execute_reply": "2024-09-06T19:33:39.463742Z" + "iopub.execute_input": "2024-09-26T14:47:41.294034Z", + "iopub.status.busy": "2024-09-26T14:47:41.293720Z", + "iopub.status.idle": "2024-09-26T14:47:41.297083Z", + "shell.execute_reply": "2024-09-26T14:47:41.296508Z" }, "nbsphinx": "hidden" }, @@ -1387,53 +1387,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "08327f8f533f49bb8518d3413af11e27": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d563179a4e534dd4b15465aa4a240b93", - "placeholder": "​", - "style": "IPY_MODEL_c0dd17ee1b414e0dbba6940c708a7553", - "tabbable": null, - "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 169MB/s]" - } - }, - "0b18f93966c84db6bd40967285652faf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0dc2781328864c55a124d3ba0119a934": { + "04992c51b0da458e880d33ce9b344519": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1451,7 +1410,7 @@ "text_color": null } }, - "0e3467cf59954459ab486aee2ba9c3a5": { + "0ef4fd968d284787a6d3621ab522b0f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1504,7 +1463,7 @@ "width": null } }, - "10945e8601a446f2bb59fa1211f86f5b": { + "14d064897b934c9fa649860e4a0d136f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1557,69 +1516,30 @@ "width": null } }, - "132a82bf8c9844f59e250a9598747c76": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "15a248e8576b4e1cace7306d79423606": { + "21a62ae51e6d4cf38d4e04afa3c63d08": { "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_9f5000cf7d6a4079b14d5f3c666d8d9a", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c12a918e266b40caa2ad3eb5ba27297c", + "layout": "IPY_MODEL_b831f552387747248fdc25346996cacd", + "placeholder": "​", + "style": "IPY_MODEL_78c7d82d53664b95a386ec96a27b6453", "tabbable": null, "tooltip": null, - "value": 128619.0 - } - }, - "214135184cdb4c61851d09d4776b8681": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 15.9M/15.9M [00:00<00:00, 75.8MB/s]" } }, - "2741fbda14764533a6d7865887e84821": { + "2d3c1dc1060c4165802a43d8a1506254": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1672,7 +1592,46 @@ "width": null } }, - "2a2e0134b1234019be47ad459a2c7e6e": { + "2e538bcb8e2a4e88a9d22def0a5b4c06": { + "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": "" + } + }, + "34e4112c8e594ad7811823c85546de7c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_59de9bdd6b4e4c99946071c77765c06e", + "placeholder": "​", + "style": "IPY_MODEL_a77c2cc33dd84816aca2750dfa77985f", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 485kB/s]" + } + }, + "355467884efe4ca683c9294faddd8e67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1725,7 +1684,39 @@ "width": null } }, - "2a4a612a6d2846bca788bddf1043cc09": { + "367f32394edb4f978af0778e4a33a113": { + "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": "" + } + }, + "3cf8422c429b489fa3609423681b6f05": { + "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": "" + } + }, + "4187151778314617bdd65072c0c39e18": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1740,16 +1731,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_30eda8d6d1724ca0a98cb1391ef57cf4", - "IPY_MODEL_31da699ce27d4748a3b0908daaeff226", - "IPY_MODEL_c979e50a880a49aeaf6e580f1f3ff7e8" + "IPY_MODEL_6aef8917b0084643af4ba12c5101c4a0", + "IPY_MODEL_db99e931b4c94d2fb4385e2ccfaa8522", + "IPY_MODEL_21a62ae51e6d4cf38d4e04afa3c63d08" ], - "layout": "IPY_MODEL_e41b0c6637564566b921cc1987c4bf9a", + "layout": "IPY_MODEL_355467884efe4ca683c9294faddd8e67", "tabbable": null, "tooltip": null } }, - "30eda8d6d1724ca0a98cb1391ef57cf4": { + "419fb750a9174f9dbee80f1946cac5e2": { + "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 + } + }, + "467e4df618db47e2aa4db7b4eeb1b07e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1764,15 +1808,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dd83345f7f5c46a38920bb555c0f6b9a", + "layout": "IPY_MODEL_0ef4fd968d284787a6d3621ab522b0f4", "placeholder": "​", - "style": "IPY_MODEL_0dc2781328864c55a124d3ba0119a934", + "style": "IPY_MODEL_fb8ea71a45ce49d1bc0bf64d3de435a1", "tabbable": null, "tooltip": null, - "value": "classifier.ckpt: 100%" + "value": " 129k/129k [00:00<00:00, 5.74MB/s]" } }, - "318a448eedbf4c68b4b67978734a1ae9": { + "4a0472fddab34db383019c14f53513c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1788,17 +1832,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0e3467cf59954459ab486aee2ba9c3a5", - "max": 2041.0, + "layout": "IPY_MODEL_6801e67ee9a2448e90812fa8fea0d356", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6c036dfeb50042c8984a6a6692fc0f9b", + "style": "IPY_MODEL_c9789735e7aa44fb8c6bcf3f8ceeebf2", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": 128619.0 } }, - "31da699ce27d4748a3b0908daaeff226": { + "54fd655fb2ea4e58ba287b8a699358a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1814,17 +1858,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c920632573cc4fe0976140070f9677ec", - "max": 15856877.0, + "layout": "IPY_MODEL_e7fc281836224420a165582ad5a1f00e", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b97eebf068ca4e9c85cbadbb4cc103c4", + "style": "IPY_MODEL_367f32394edb4f978af0778e4a33a113", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": 16887676.0 } }, - "46e0a3db3d4b44aea458c9d15b1b45b8": { + "5704c0dc0c934dab903a45047e08bfa1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1877,30 +1921,7 @@ "width": null } }, - "4c2a6bff30b84735af3cf83ab6de7ddf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_46e0a3db3d4b44aea458c9d15b1b45b8", - "placeholder": "​", - "style": "IPY_MODEL_b420c63e4f174bba8f48175457a23dfe", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 804kB/s]" - } - }, - "4c6822f78a694818a65c111aed9cd2c2": { + "59de9bdd6b4e4c99946071c77765c06e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1953,120 +1974,43 @@ "width": null } }, - "53dafb78e671406e89f3754d23b34684": { + "62b1b0b90a684d3488af171459b2e05b": { "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_ef0ba41ae31d4fca866f944d42378821", - "IPY_MODEL_318a448eedbf4c68b4b67978734a1ae9", - "IPY_MODEL_6fc0bc69349045329fcb28a46a2fe14b" - ], - "layout": "IPY_MODEL_2a2e0134b1234019be47ad459a2c7e6e", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5aded6b96abc4bfebf8a38a3dcad2d6f": { + "67269dc6c7a64c2a85d31a7de8c68e5a": { "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_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7ba99fd178de4f25867b6e37949a6d85", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f321f0e3281b4b478dffcfb404472020", - "tabbable": null, - "tooltip": null, - "value": 3201.0 - } - }, - "5ee4ab99d955426daf559df8bf71c44f": { - "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_b4b3cc9f4e4c408998a1c0ebec86d5bc", - "IPY_MODEL_5aded6b96abc4bfebf8a38a3dcad2d6f", - "IPY_MODEL_4c2a6bff30b84735af3cf83ab6de7ddf" - ], - "layout": "IPY_MODEL_6eec35a271e4402b8f28109615706306", - "tabbable": null, - "tooltip": null - } - }, - "6a89c26dda78427781977305cd34e44b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f948f7fae2bd4b23b89c1a9f86de6cdc", - "placeholder": "​", - "style": "IPY_MODEL_0b18f93966c84db6bd40967285652faf", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "6c036dfeb50042c8984a6a6692fc0f9b": { - "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": "@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 } }, - "6eec35a271e4402b8f28109615706306": { + "6801e67ee9a2448e90812fa8fea0d356": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2119,7 +2063,7 @@ "width": null } }, - "6fc0bc69349045329fcb28a46a2fe14b": { + "6aef8917b0084643af4ba12c5101c4a0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2134,68 +2078,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2741fbda14764533a6d7865887e84821", + "layout": "IPY_MODEL_d12f8fa702da44f6b81f1b84c1c76383", "placeholder": "​", - "style": "IPY_MODEL_132a82bf8c9844f59e250a9598747c76", + "style": "IPY_MODEL_6cd21c0f3c7d4b668b81735903eedb1b", "tabbable": null, "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 482kB/s]" + "value": "classifier.ckpt: 100%" } }, - "700bb6482b2c4111b4ed9390c8470861": { - "model_module": "@jupyter-widgets/base", + "6cd21c0f3c7d4b668b81735903eedb1b": { + "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 } }, - "701065566fa2417290be013d76599838": { + "73080f2ef1784e73947c31dfebce4e08": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2248,7 +2157,7 @@ "width": null } }, - "71c1b39c84cf47b5a477e26da271fef9": { + "73e424d16cbf4e40ad3206856f075d5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2301,7 +2210,30 @@ "width": null } }, - "798cb5dd7eb545d5a4188e416dce6f88": { + "78baba5e6db44fa6a19c704bf00fb60c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f841644ac2b04256b2f3c4ad9a4db82f", + "placeholder": "​", + "style": "IPY_MODEL_67269dc6c7a64c2a85d31a7de8c68e5a", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "78c7d82d53664b95a386ec96a27b6453": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2319,7 +2251,31 @@ "text_color": null } }, - "7ba99fd178de4f25867b6e37949a6d85": { + "7aac7a5b86074e509ae85dca28a96569": { + "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_99009d85bc164733af718beec07a0dd3", + "IPY_MODEL_4a0472fddab34db383019c14f53513c2", + "IPY_MODEL_467e4df618db47e2aa4db7b4eeb1b07e" + ], + "layout": "IPY_MODEL_14d064897b934c9fa649860e4a0d136f", + "tabbable": null, + "tooltip": null + } + }, + "7f6ff60608894304be071471768422d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2372,7 +2328,31 @@ "width": null } }, - "7ca38434b4e44b5b86b173c7855b4ff3": { + "86394b0091ab4767834e877eb97c7f29": { + "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_fdc61f1ca4794f988ad26aa36f51cded", + "IPY_MODEL_c26558f6f8394f7483fa04455db2ead3", + "IPY_MODEL_34e4112c8e594ad7811823c85546de7c" + ], + "layout": "IPY_MODEL_da43246f2ddc4df9968898f36b1bfc0c", + "tabbable": null, + "tooltip": null + } + }, + "89ca8bee4d29420391f60780a4e5bb37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2390,7 +2370,7 @@ "text_color": null } }, - "819ab4db9b9249cfb05199eec4ffe2ae": { + "8c24d36de7d04ba8a98dbc6243bffa1e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2443,7 +2423,31 @@ "width": null } }, - "8b0da1e1f92449e49814a4792b3a0a18": { + "9001078dd11947c3b743e880d41d89e0": { + "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_78baba5e6db44fa6a19c704bf00fb60c", + "IPY_MODEL_99c304eddb9d4f808e1444eda066b707", + "IPY_MODEL_be4a812783c5484dbde2feef6424ebe3" + ], + "layout": "IPY_MODEL_9198ec1e4cbc45a5bbf7c1f0e8976f79", + "tabbable": null, + "tooltip": null + } + }, + "9198ec1e4cbc45a5bbf7c1f0e8976f79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2496,7 +2500,46 @@ "width": null } }, - "9a1d64682df846808b68e96652571971": { + "946fc47d18224c0287afaa056d02fa86": { + "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": "" + } + }, + "99009d85bc164733af718beec07a0dd3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_73080f2ef1784e73947c31dfebce4e08", + "placeholder": "​", + "style": "IPY_MODEL_fe5e77a095e54cad8e7f53fae0f07637", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" + } + }, + "99c304eddb9d4f808e1444eda066b707": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2512,17 +2555,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8b0da1e1f92449e49814a4792b3a0a18", - "max": 16887676.0, + "layout": "IPY_MODEL_b67e2b3c22e243868a621e764d7615bd", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_fa5d63111e8040c8a46a0ef606a7b541", + "style": "IPY_MODEL_3cf8422c429b489fa3609423681b6f05", "tabbable": null, "tooltip": null, - "value": 16887676.0 + "value": 3201.0 } }, - "9f5000cf7d6a4079b14d5f3c666d8d9a": { + "a7073edae4e4487fb17ec295efaf2195": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2575,30 +2618,7 @@ "width": null } }, - "a79c4f7046b44587aab8c79e98299302": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_701065566fa2417290be013d76599838", - "placeholder": "​", - "style": "IPY_MODEL_798cb5dd7eb545d5a4188e416dce6f88", - "tabbable": null, - "tooltip": null, - "value": "label_encoder.txt: 100%" - } - }, - "b420c63e4f174bba8f48175457a23dfe": { + "a77c2cc33dd84816aca2750dfa77985f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2616,30 +2636,31 @@ "text_color": null } }, - "b4b3cc9f4e4c408998a1c0ebec86d5bc": { + "aea522f486884287934824875f65568c": { "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_71c1b39c84cf47b5a477e26da271fef9", - "placeholder": "​", - "style": "IPY_MODEL_7ca38434b4e44b5b86b173c7855b4ff3", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e83d24db49ae4489ac3ce5c785ee2c56", + "IPY_MODEL_54fd655fb2ea4e58ba287b8a699358a4", + "IPY_MODEL_fb154ca6183045b2bcadfc5b92658f12" + ], + "layout": "IPY_MODEL_5704c0dc0c934dab903a45047e08bfa1", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" + "tooltip": null } }, - "b8e950254c7c4bfc904715885aa32fa1": { + "b67e2b3c22e243868a621e764d7615bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2692,57 +2713,7 @@ "width": null } }, - "b97eebf068ca4e9c85cbadbb4cc103c4": { - "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": "" - } - }, - "c0dd17ee1b414e0dbba6940c708a7553": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c12a918e266b40caa2ad3eb5ba27297c": { - "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": "" - } - }, - "c920632573cc4fe0976140070f9677ec": { + "b831f552387747248fdc25346996cacd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2795,7 +2766,7 @@ "width": null } }, - "c979e50a880a49aeaf6e580f1f3ff7e8": { + "be4a812783c5484dbde2feef6424ebe3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2810,15 +2781,75 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4c6822f78a694818a65c111aed9cd2c2", + "layout": "IPY_MODEL_8c24d36de7d04ba8a98dbc6243bffa1e", "placeholder": "​", - "style": "IPY_MODEL_e3c6b0d1ab4745c796d69b14cb880bf5", + "style": "IPY_MODEL_c4f6d1fdbf7f4c11b3b58973c52c1d0d", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 212MB/s]" + "value": " 3.20k/3.20k [00:00<00:00, 792kB/s]" } }, - "d563179a4e534dd4b15465aa4a240b93": { + "c26558f6f8394f7483fa04455db2ead3": { + "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_2d3c1dc1060c4165802a43d8a1506254", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_946fc47d18224c0287afaa056d02fa86", + "tabbable": null, + "tooltip": null, + "value": 2041.0 + } + }, + "c4f6d1fdbf7f4c11b3b58973c52c1d0d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c9789735e7aa44fb8c6bcf3f8ceeebf2": { + "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": "" + } + }, + "d12f8fa702da44f6b81f1b84c1c76383": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2871,7 +2902,7 @@ "width": null } }, - "dd83345f7f5c46a38920bb555c0f6b9a": { + "da43246f2ddc4df9968898f36b1bfc0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2924,48 +2955,33 @@ "width": null } }, - "de328081cf5143bda2f18a1f732277b0": { + "db99e931b4c94d2fb4385e2ccfaa8522": { "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_10945e8601a446f2bb59fa1211f86f5b", - "placeholder": "​", - "style": "IPY_MODEL_214135184cdb4c61851d09d4776b8681", + "layout": "IPY_MODEL_73e424d16cbf4e40ad3206856f075d5b", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2e538bcb8e2a4e88a9d22def0a5b4c06", "tabbable": null, "tooltip": null, - "value": " 129k/129k [00:00<00:00, 24.4MB/s]" - } - }, - "e3c6b0d1ab4745c796d69b14cb880bf5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": 15856877.0 } }, - "e41b0c6637564566b921cc1987c4bf9a": { + "e7fc281836224420a165582ad5a1f00e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3018,31 +3034,7 @@ "width": null } }, - "e4e9c1fa715d49009ec1097cf561d5f6": { - "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_6a89c26dda78427781977305cd34e44b", - "IPY_MODEL_9a1d64682df846808b68e96652571971", - "IPY_MODEL_08327f8f533f49bb8518d3413af11e27" - ], - "layout": "IPY_MODEL_819ab4db9b9249cfb05199eec4ffe2ae", - "tabbable": null, - "tooltip": null - } - }, - "ef0ba41ae31d4fca866f944d42378821": { + "e83d24db49ae4489ac3ce5c785ee2c56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3057,55 +3049,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_700bb6482b2c4111b4ed9390c8470861", + "layout": "IPY_MODEL_7f6ff60608894304be071471768422d5", "placeholder": "​", - "style": "IPY_MODEL_f9b3a44be9f34f6e93de758ee46b92ec", + "style": "IPY_MODEL_89ca8bee4d29420391f60780a4e5bb37", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "f321f0e3281b4b478dffcfb404472020": { - "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": "" - } - }, - "f814d21bb5204a479acad09d629678fa": { - "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_a79c4f7046b44587aab8c79e98299302", - "IPY_MODEL_15a248e8576b4e1cace7306d79423606", - "IPY_MODEL_de328081cf5143bda2f18a1f732277b0" - ], - "layout": "IPY_MODEL_b8e950254c7c4bfc904715885aa32fa1", - "tabbable": null, - "tooltip": null + "value": "embedding_model.ckpt: 100%" } }, - "f948f7fae2bd4b23b89c1a9f86de6cdc": { + "f841644ac2b04256b2f3c4ad9a4db82f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3158,7 +3110,30 @@ "width": null } }, - "f9b3a44be9f34f6e93de758ee46b92ec": { + "fb154ca6183045b2bcadfc5b92658f12": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a7073edae4e4487fb17ec295efaf2195", + "placeholder": "​", + "style": "IPY_MODEL_62b1b0b90a684d3488af171459b2e05b", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 56.5MB/s]" + } + }, + "fb8ea71a45ce49d1bc0bf64d3de435a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3176,20 +3151,45 @@ "text_color": null } }, - "fa5d63111e8040c8a46a0ef606a7b541": { + "fdc61f1ca4794f988ad26aa36f51cded": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_419fb750a9174f9dbee80f1946cac5e2", + "placeholder": "​", + "style": "IPY_MODEL_04992c51b0da458e880d33ce9b344519", + "tabbable": null, + "tooltip": null, + "value": "hyperparams.yaml: 100%" + } + }, + "fe5e77a095e54cad8e7f53fae0f07637": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 1028deca4..f581760ef 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": "2024-09-06T19:33:42.774016Z", - "iopub.status.busy": "2024-09-06T19:33:42.773836Z", - "iopub.status.idle": "2024-09-06T19:33:43.987649Z", - "shell.execute_reply": "2024-09-06T19:33:43.987087Z" + "iopub.execute_input": "2024-09-26T14:47:45.611697Z", + "iopub.status.busy": "2024-09-26T14:47:45.611515Z", + "iopub.status.idle": "2024-09-26T14:47:46.872000Z", + "shell.execute_reply": "2024-09-26T14:47:46.871368Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:43.990323Z", - "iopub.status.busy": "2024-09-06T19:33:43.989863Z", - "iopub.status.idle": "2024-09-06T19:33:43.992901Z", - "shell.execute_reply": "2024-09-06T19:33:43.992377Z" + "iopub.execute_input": "2024-09-26T14:47:46.874219Z", + "iopub.status.busy": "2024-09-26T14:47:46.873943Z", + "iopub.status.idle": "2024-09-26T14:47:46.877182Z", + "shell.execute_reply": "2024-09-26T14:47:46.876630Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:43.995557Z", - "iopub.status.busy": "2024-09-06T19:33:43.995114Z", - "iopub.status.idle": "2024-09-06T19:33:44.005313Z", - "shell.execute_reply": "2024-09-06T19:33:44.004712Z" + "iopub.execute_input": "2024-09-26T14:47:46.878965Z", + "iopub.status.busy": "2024-09-26T14:47:46.878784Z", + "iopub.status.idle": "2024-09-26T14:47:46.887523Z", + "shell.execute_reply": "2024-09-26T14:47:46.887072Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.007445Z", - "iopub.status.busy": "2024-09-06T19:33:44.007143Z", - "iopub.status.idle": "2024-09-06T19:33:44.012113Z", - "shell.execute_reply": "2024-09-06T19:33:44.011528Z" + "iopub.execute_input": "2024-09-26T14:47:46.889337Z", + "iopub.status.busy": "2024-09-26T14:47:46.889146Z", + "iopub.status.idle": "2024-09-26T14:47:46.893734Z", + "shell.execute_reply": "2024-09-26T14:47:46.893242Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.014273Z", - "iopub.status.busy": "2024-09-06T19:33:44.013973Z", - "iopub.status.idle": "2024-09-06T19:33:44.198978Z", - "shell.execute_reply": "2024-09-06T19:33:44.198432Z" + "iopub.execute_input": "2024-09-26T14:47:46.895681Z", + "iopub.status.busy": "2024-09-26T14:47:46.895273Z", + "iopub.status.idle": "2024-09-26T14:47:47.085029Z", + "shell.execute_reply": "2024-09-26T14:47:47.084376Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.201674Z", - "iopub.status.busy": "2024-09-06T19:33:44.201189Z", - "iopub.status.idle": "2024-09-06T19:33:44.572697Z", - "shell.execute_reply": "2024-09-06T19:33:44.572078Z" + "iopub.execute_input": "2024-09-26T14:47:47.087608Z", + "iopub.status.busy": "2024-09-26T14:47:47.087115Z", + "iopub.status.idle": "2024-09-26T14:47:47.421574Z", + "shell.execute_reply": "2024-09-26T14:47:47.420964Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.574957Z", - "iopub.status.busy": "2024-09-06T19:33:44.574618Z", - "iopub.status.idle": "2024-09-06T19:33:44.598417Z", - "shell.execute_reply": "2024-09-06T19:33:44.597853Z" + "iopub.execute_input": "2024-09-26T14:47:47.423413Z", + "iopub.status.busy": "2024-09-26T14:47:47.423220Z", + "iopub.status.idle": "2024-09-26T14:47:47.447494Z", + "shell.execute_reply": "2024-09-26T14:47:47.447007Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.600565Z", - "iopub.status.busy": "2024-09-06T19:33:44.600242Z", - "iopub.status.idle": "2024-09-06T19:33:44.611542Z", - "shell.execute_reply": "2024-09-06T19:33:44.611124Z" + "iopub.execute_input": "2024-09-26T14:47:47.449677Z", + "iopub.status.busy": "2024-09-26T14:47:47.449174Z", + "iopub.status.idle": "2024-09-26T14:47:47.542304Z", + "shell.execute_reply": "2024-09-26T14:47:47.541668Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.613571Z", - "iopub.status.busy": "2024-09-06T19:33:44.613277Z", - "iopub.status.idle": "2024-09-06T19:33:46.668917Z", - "shell.execute_reply": "2024-09-06T19:33:46.668293Z" + "iopub.execute_input": "2024-09-26T14:47:47.544443Z", + "iopub.status.busy": "2024-09-26T14:47:47.544249Z", + "iopub.status.idle": "2024-09-26T14:47:49.580917Z", + "shell.execute_reply": "2024-09-26T14:47:49.580350Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.671429Z", - "iopub.status.busy": "2024-09-06T19:33:46.670969Z", - "iopub.status.idle": "2024-09-06T19:33:46.692283Z", - "shell.execute_reply": "2024-09-06T19:33:46.691706Z" + "iopub.execute_input": "2024-09-26T14:47:49.583198Z", + "iopub.status.busy": "2024-09-26T14:47:49.582700Z", + "iopub.status.idle": "2024-09-26T14:47:49.606247Z", + "shell.execute_reply": "2024-09-26T14:47:49.605757Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.694643Z", - "iopub.status.busy": "2024-09-06T19:33:46.694120Z", - "iopub.status.idle": "2024-09-06T19:33:46.711969Z", - "shell.execute_reply": "2024-09-06T19:33:46.711526Z" + "iopub.execute_input": "2024-09-26T14:47:49.608105Z", + "iopub.status.busy": "2024-09-26T14:47:49.607920Z", + "iopub.status.idle": "2024-09-26T14:47:49.626258Z", + "shell.execute_reply": "2024-09-26T14:47:49.625780Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.713865Z", - "iopub.status.busy": "2024-09-06T19:33:46.713694Z", - "iopub.status.idle": "2024-09-06T19:33:46.728067Z", - "shell.execute_reply": "2024-09-06T19:33:46.727609Z" + "iopub.execute_input": "2024-09-26T14:47:49.628144Z", + "iopub.status.busy": "2024-09-26T14:47:49.627801Z", + "iopub.status.idle": "2024-09-26T14:47:49.641555Z", + "shell.execute_reply": "2024-09-26T14:47:49.641075Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.729970Z", - "iopub.status.busy": "2024-09-06T19:33:46.729797Z", - "iopub.status.idle": "2024-09-06T19:33:46.748313Z", - "shell.execute_reply": "2024-09-06T19:33:46.747746Z" + "iopub.execute_input": "2024-09-26T14:47:49.643368Z", + "iopub.status.busy": "2024-09-26T14:47:49.643024Z", + "iopub.status.idle": "2024-09-26T14:47:49.664797Z", + "shell.execute_reply": "2024-09-26T14:47:49.664316Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8a80b5b1ace4f0e9399969281df7d06", + "model_id": "84640571afb64f84bab623cedce4b8ca", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.750540Z", - "iopub.status.busy": "2024-09-06T19:33:46.750202Z", - "iopub.status.idle": "2024-09-06T19:33:46.765277Z", - "shell.execute_reply": "2024-09-06T19:33:46.764810Z" + "iopub.execute_input": "2024-09-26T14:47:49.666763Z", + "iopub.status.busy": "2024-09-26T14:47:49.666313Z", + "iopub.status.idle": "2024-09-26T14:47:49.681745Z", + "shell.execute_reply": "2024-09-26T14:47:49.681144Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.767376Z", - "iopub.status.busy": "2024-09-06T19:33:46.767048Z", - "iopub.status.idle": "2024-09-06T19:33:46.772946Z", - "shell.execute_reply": "2024-09-06T19:33:46.772447Z" + "iopub.execute_input": "2024-09-26T14:47:49.683800Z", + "iopub.status.busy": "2024-09-26T14:47:49.683338Z", + "iopub.status.idle": "2024-09-26T14:47:49.689263Z", + "shell.execute_reply": "2024-09-26T14:47:49.688776Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.774871Z", - "iopub.status.busy": "2024-09-06T19:33:46.774606Z", - "iopub.status.idle": "2024-09-06T19:33:46.792743Z", - "shell.execute_reply": "2024-09-06T19:33:46.792272Z" + "iopub.execute_input": "2024-09-26T14:47:49.691065Z", + "iopub.status.busy": "2024-09-26T14:47:49.690751Z", + "iopub.status.idle": "2024-09-26T14:47:49.709104Z", + "shell.execute_reply": "2024-09-26T14:47:49.708511Z" } }, "outputs": [ @@ -1437,7 +1437,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { @@ -1447,51 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1b84860876254aa29989a5bb614dca8d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4440624d361b4a39a470c6b36d42b8d3": { - "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_ec547a92716a409bb8eb86bc364258c9", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ba52f0b8569f404584f54443a28a0baf", - "tabbable": null, - "tooltip": null, - "value": 132.0 - } - }, - "55ed31e23a4a443bbb5e734bb143d697": { + "37952b8d6afc47f2b9ed08ee7e66d264": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1544,53 +1500,49 @@ "width": null } }, - "890c901184904e0883bb0bded31d86de": { + "48eeb0e9ec1b440e9b49518e8338af15": { "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_55ed31e23a4a443bbb5e734bb143d697", - "placeholder": "​", - "style": "IPY_MODEL_fd6266d59b69432181af01e5eb3c389d", - "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 } }, - "8e898dc9dd204b2f8ba985adc383a396": { + "84640571afb64f84bab623cedce4b8ca": { "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_eae4cf97733c4548920387dc447b9d98", - "placeholder": "​", - "style": "IPY_MODEL_1b84860876254aa29989a5bb614dca8d", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b144d01abdc746e495df91b32d9d09e8", + "IPY_MODEL_ec22ebaf4e91499784ef8ac8e966a147", + "IPY_MODEL_f4567689e9ee49d28457ae1b5cdc262a" + ], + "layout": "IPY_MODEL_9602309f72214bd0b17e007cb02789b7", "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 13525.06 examples/s]" + "tooltip": null } }, - "94329361a5b3479e804741fa80c47e78": { + "9602309f72214bd0b17e007cb02789b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1643,47 +1595,30 @@ "width": null } }, - "ba52f0b8569f404584f54443a28a0baf": { - "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": "" - } - }, - "e8a80b5b1ace4f0e9399969281df7d06": { + "b144d01abdc746e495df91b32d9d09e8": { "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_890c901184904e0883bb0bded31d86de", - "IPY_MODEL_4440624d361b4a39a470c6b36d42b8d3", - "IPY_MODEL_8e898dc9dd204b2f8ba985adc383a396" - ], - "layout": "IPY_MODEL_94329361a5b3479e804741fa80c47e78", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_37952b8d6afc47f2b9ed08ee7e66d264", + "placeholder": "​", + "style": "IPY_MODEL_da7692e003c04b5f8f1de02ea7e688c1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "eae4cf97733c4548920387dc447b9d98": { + "c51595d098f645f0a7967c34f586fa27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1736,7 +1671,67 @@ "width": null } }, - "ec547a92716a409bb8eb86bc364258c9": { + "da7692e003c04b5f8f1de02ea7e688c1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "e56307db7d2e450c9f2c4b97981eee9d": { + "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": "" + } + }, + "ec22ebaf4e91499784ef8ac8e966a147": { + "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_ed4df9f1de274611a1946fa0e269d33e", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e56307db7d2e450c9f2c4b97981eee9d", + "tabbable": null, + "tooltip": null, + "value": 132.0 + } + }, + "ed4df9f1de274611a1946fa0e269d33e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1789,22 +1784,27 @@ "width": null } }, - "fd6266d59b69432181af01e5eb3c389d": { + "f4567689e9ee49d28457ae1b5cdc262a": { "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_c51595d098f645f0a7967c34f586fa27", + "placeholder": "​", + "style": "IPY_MODEL_48eeb0e9ec1b440e9b49518e8338af15", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 11171.72 examples/s]" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index a75ce5d83..767bffe7d 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": "2024-09-06T19:33:49.692668Z", - "iopub.status.busy": "2024-09-06T19:33:49.692505Z", - "iopub.status.idle": "2024-09-06T19:33:50.890931Z", - "shell.execute_reply": "2024-09-06T19:33:50.890368Z" + "iopub.execute_input": "2024-09-26T14:47:52.531871Z", + "iopub.status.busy": "2024-09-26T14:47:52.531690Z", + "iopub.status.idle": "2024-09-26T14:47:53.801759Z", + "shell.execute_reply": "2024-09-26T14:47:53.801164Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:50.893481Z", - "iopub.status.busy": "2024-09-06T19:33:50.892976Z", - "iopub.status.idle": "2024-09-06T19:33:50.895994Z", - "shell.execute_reply": "2024-09-06T19:33:50.895546Z" + "iopub.execute_input": "2024-09-26T14:47:53.804080Z", + "iopub.status.busy": "2024-09-26T14:47:53.803489Z", + "iopub.status.idle": "2024-09-26T14:47:53.806671Z", + "shell.execute_reply": "2024-09-26T14:47:53.806214Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.898095Z", - "iopub.status.busy": "2024-09-06T19:33:50.897919Z", - "iopub.status.idle": "2024-09-06T19:33:50.907050Z", - "shell.execute_reply": "2024-09-06T19:33:50.906577Z" + "iopub.execute_input": "2024-09-26T14:47:53.808602Z", + "iopub.status.busy": "2024-09-26T14:47:53.808274Z", + "iopub.status.idle": "2024-09-26T14:47:53.817439Z", + "shell.execute_reply": "2024-09-26T14:47:53.816846Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.908860Z", - "iopub.status.busy": "2024-09-06T19:33:50.908672Z", - "iopub.status.idle": "2024-09-06T19:33:50.913284Z", - "shell.execute_reply": "2024-09-06T19:33:50.912693Z" + "iopub.execute_input": "2024-09-26T14:47:53.819218Z", + "iopub.status.busy": "2024-09-26T14:47:53.818813Z", + "iopub.status.idle": "2024-09-26T14:47:53.823869Z", + "shell.execute_reply": "2024-09-26T14:47:53.823416Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.915417Z", - "iopub.status.busy": "2024-09-06T19:33:50.915238Z", - "iopub.status.idle": "2024-09-06T19:33:51.099306Z", - "shell.execute_reply": "2024-09-06T19:33:51.098789Z" + "iopub.execute_input": "2024-09-26T14:47:53.825624Z", + "iopub.status.busy": "2024-09-26T14:47:53.825446Z", + "iopub.status.idle": "2024-09-26T14:47:54.012981Z", + "shell.execute_reply": "2024-09-26T14:47:54.012359Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.101790Z", - "iopub.status.busy": "2024-09-06T19:33:51.101450Z", - "iopub.status.idle": "2024-09-06T19:33:51.473593Z", - "shell.execute_reply": "2024-09-06T19:33:51.473003Z" + "iopub.execute_input": "2024-09-26T14:47:54.015114Z", + "iopub.status.busy": "2024-09-26T14:47:54.014923Z", + "iopub.status.idle": "2024-09-26T14:47:54.396149Z", + "shell.execute_reply": "2024-09-26T14:47:54.395584Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.475866Z", - "iopub.status.busy": "2024-09-06T19:33:51.475414Z", - "iopub.status.idle": "2024-09-06T19:33:51.478399Z", - "shell.execute_reply": "2024-09-06T19:33:51.477816Z" + "iopub.execute_input": "2024-09-26T14:47:54.398064Z", + "iopub.status.busy": "2024-09-26T14:47:54.397698Z", + "iopub.status.idle": "2024-09-26T14:47:54.400577Z", + "shell.execute_reply": "2024-09-26T14:47:54.400116Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.480745Z", - "iopub.status.busy": "2024-09-06T19:33:51.480341Z", - "iopub.status.idle": "2024-09-06T19:33:51.514306Z", - "shell.execute_reply": "2024-09-06T19:33:51.513859Z" + "iopub.execute_input": "2024-09-26T14:47:54.402366Z", + "iopub.status.busy": "2024-09-26T14:47:54.402019Z", + "iopub.status.idle": "2024-09-26T14:47:54.437650Z", + "shell.execute_reply": "2024-09-26T14:47:54.437009Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.516441Z", - "iopub.status.busy": "2024-09-06T19:33:51.516020Z", - "iopub.status.idle": "2024-09-06T19:33:53.590850Z", - "shell.execute_reply": "2024-09-06T19:33:53.590263Z" + "iopub.execute_input": "2024-09-26T14:47:54.439903Z", + "iopub.status.busy": "2024-09-26T14:47:54.439564Z", + "iopub.status.idle": "2024-09-26T14:47:56.611360Z", + "shell.execute_reply": "2024-09-26T14:47:56.610777Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.593403Z", - "iopub.status.busy": "2024-09-06T19:33:53.592894Z", - "iopub.status.idle": "2024-09-06T19:33:53.611543Z", - "shell.execute_reply": "2024-09-06T19:33:53.610984Z" + "iopub.execute_input": "2024-09-26T14:47:56.613533Z", + "iopub.status.busy": "2024-09-26T14:47:56.612962Z", + "iopub.status.idle": "2024-09-26T14:47:56.632035Z", + "shell.execute_reply": "2024-09-26T14:47:56.631583Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.613666Z", - "iopub.status.busy": "2024-09-06T19:33:53.613354Z", - "iopub.status.idle": "2024-09-06T19:33:53.619845Z", - "shell.execute_reply": "2024-09-06T19:33:53.619296Z" + "iopub.execute_input": "2024-09-26T14:47:56.633836Z", + "iopub.status.busy": "2024-09-26T14:47:56.633486Z", + "iopub.status.idle": "2024-09-26T14:47:56.639903Z", + "shell.execute_reply": "2024-09-26T14:47:56.639463Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.621866Z", - "iopub.status.busy": "2024-09-06T19:33:53.621559Z", - "iopub.status.idle": "2024-09-06T19:33:53.628504Z", - "shell.execute_reply": "2024-09-06T19:33:53.627959Z" + "iopub.execute_input": "2024-09-26T14:47:56.641707Z", + "iopub.status.busy": "2024-09-26T14:47:56.641368Z", + "iopub.status.idle": "2024-09-26T14:47:56.647367Z", + "shell.execute_reply": "2024-09-26T14:47:56.646803Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.630721Z", - "iopub.status.busy": "2024-09-06T19:33:53.630404Z", - "iopub.status.idle": "2024-09-06T19:33:53.640976Z", - "shell.execute_reply": "2024-09-06T19:33:53.640522Z" + "iopub.execute_input": "2024-09-26T14:47:56.649030Z", + "iopub.status.busy": "2024-09-26T14:47:56.648855Z", + "iopub.status.idle": "2024-09-26T14:47:56.659402Z", + "shell.execute_reply": "2024-09-26T14:47:56.658957Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.643037Z", - "iopub.status.busy": "2024-09-06T19:33:53.642719Z", - "iopub.status.idle": "2024-09-06T19:33:53.651678Z", - "shell.execute_reply": "2024-09-06T19:33:53.651115Z" + "iopub.execute_input": "2024-09-26T14:47:56.661038Z", + "iopub.status.busy": "2024-09-26T14:47:56.660772Z", + "iopub.status.idle": "2024-09-26T14:47:56.669970Z", + "shell.execute_reply": "2024-09-26T14:47:56.669408Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.653852Z", - "iopub.status.busy": "2024-09-06T19:33:53.653447Z", - "iopub.status.idle": "2024-09-06T19:33:53.660374Z", - "shell.execute_reply": "2024-09-06T19:33:53.659816Z" + "iopub.execute_input": "2024-09-26T14:47:56.671720Z", + "iopub.status.busy": "2024-09-26T14:47:56.671331Z", + "iopub.status.idle": "2024-09-26T14:47:56.678074Z", + "shell.execute_reply": "2024-09-26T14:47:56.677629Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.662428Z", - "iopub.status.busy": "2024-09-06T19:33:53.662108Z", - "iopub.status.idle": "2024-09-06T19:33:53.671181Z", - "shell.execute_reply": "2024-09-06T19:33:53.670717Z" + "iopub.execute_input": "2024-09-26T14:47:56.679875Z", + "iopub.status.busy": "2024-09-26T14:47:56.679423Z", + "iopub.status.idle": "2024-09-26T14:47:56.689042Z", + "shell.execute_reply": "2024-09-26T14:47:56.688474Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.673080Z", - "iopub.status.busy": "2024-09-06T19:33:53.672905Z", - "iopub.status.idle": "2024-09-06T19:33:53.689334Z", - "shell.execute_reply": "2024-09-06T19:33:53.688736Z" + "iopub.execute_input": "2024-09-26T14:47:56.690832Z", + "iopub.status.busy": "2024-09-26T14:47:56.690435Z", + "iopub.status.idle": "2024-09-26T14:47:56.707572Z", + "shell.execute_reply": "2024-09-26T14:47:56.706989Z" }, "nbsphinx": "hidden" }, @@ -1648,7 +1648,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index d0e19982d..81904fb5e 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": "2024-09-06T19:33:56.342254Z", - "iopub.status.busy": "2024-09-06T19:33:56.341754Z", - "iopub.status.idle": "2024-09-06T19:33:59.356819Z", - "shell.execute_reply": "2024-09-06T19:33:59.356183Z" + "iopub.execute_input": "2024-09-26T14:47:59.485196Z", + "iopub.status.busy": "2024-09-26T14:47:59.485011Z", + "iopub.status.idle": "2024-09-26T14:48:02.622875Z", + "shell.execute_reply": "2024-09-26T14:48:02.622306Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:59.359528Z", - "iopub.status.busy": "2024-09-06T19:33:59.359236Z", - "iopub.status.idle": "2024-09-06T19:33:59.363077Z", - "shell.execute_reply": "2024-09-06T19:33:59.362504Z" + "iopub.execute_input": "2024-09-26T14:48:02.625172Z", + "iopub.status.busy": "2024-09-26T14:48:02.624685Z", + "iopub.status.idle": "2024-09-26T14:48:02.628360Z", + "shell.execute_reply": "2024-09-26T14:48:02.627898Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:59.365205Z", - "iopub.status.busy": "2024-09-06T19:33:59.364886Z", - "iopub.status.idle": "2024-09-06T19:34:04.314293Z", - "shell.execute_reply": "2024-09-06T19:34:04.313807Z" + "iopub.execute_input": "2024-09-26T14:48:02.630127Z", + "iopub.status.busy": "2024-09-26T14:48:02.629815Z", + "iopub.status.idle": "2024-09-26T14:48:05.745420Z", + "shell.execute_reply": "2024-09-26T14:48:05.744938Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bcf07287e5846bcade12829a0129e5a", + "model_id": "517b83c613bb49c9ab0cd319caf77fa4", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3273abc0b1474d17ad8e620a0b9cd685", + "model_id": "157b0b5de92c4dd39861100e0048c0b7", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "468f054b84de4a46abae17b5d6030a66", + "model_id": "dc9f46273db144d982f466b186d6ea8d", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85a6da0e361d4bb78dac486525795dad", + "model_id": "77e4c9ec41b8452c8963af0b77b5555f", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16923bdba0af47908931030b52eaedca", + "model_id": "cd9cd1e986f74424b60db3510b43826d", "version_major": 2, "version_minor": 0 }, @@ -260,10 +260,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:04.316479Z", - "iopub.status.busy": "2024-09-06T19:34:04.316130Z", - "iopub.status.idle": "2024-09-06T19:34:04.319984Z", - "shell.execute_reply": "2024-09-06T19:34:04.319538Z" + "iopub.execute_input": "2024-09-26T14:48:05.747100Z", + "iopub.status.busy": "2024-09-26T14:48:05.746916Z", + "iopub.status.idle": "2024-09-26T14:48:05.750921Z", + "shell.execute_reply": "2024-09-26T14:48:05.750358Z" } }, "outputs": [ @@ -288,17 +288,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:04.321997Z", - "iopub.status.busy": "2024-09-06T19:34:04.321665Z", - "iopub.status.idle": "2024-09-06T19:34:15.824023Z", - "shell.execute_reply": "2024-09-06T19:34:15.823467Z" + "iopub.execute_input": "2024-09-26T14:48:05.752502Z", + "iopub.status.busy": "2024-09-26T14:48:05.752202Z", + "iopub.status.idle": "2024-09-26T14:48:17.148896Z", + "shell.execute_reply": "2024-09-26T14:48:17.148237Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bfcb4b6339d14370bc404a61e757edfd", + "model_id": "5e114e4103d94679910f2192af574f94", "version_major": 2, "version_minor": 0 }, @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:15.826734Z", - "iopub.status.busy": "2024-09-06T19:34:15.826342Z", - "iopub.status.idle": "2024-09-06T19:34:34.591212Z", - "shell.execute_reply": "2024-09-06T19:34:34.590672Z" + "iopub.execute_input": "2024-09-26T14:48:17.151191Z", + "iopub.status.busy": "2024-09-26T14:48:17.150948Z", + "iopub.status.idle": "2024-09-26T14:48:35.261693Z", + "shell.execute_reply": "2024-09-26T14:48:35.261069Z" } }, "outputs": [], @@ -372,10 +372,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.593912Z", - "iopub.status.busy": "2024-09-06T19:34:34.593533Z", - "iopub.status.idle": "2024-09-06T19:34:34.599439Z", - "shell.execute_reply": "2024-09-06T19:34:34.598956Z" + "iopub.execute_input": "2024-09-26T14:48:35.264037Z", + "iopub.status.busy": "2024-09-26T14:48:35.263651Z", + "iopub.status.idle": "2024-09-26T14:48:35.269633Z", + "shell.execute_reply": "2024-09-26T14:48:35.269145Z" } }, "outputs": [], @@ -413,10 +413,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.601473Z", - "iopub.status.busy": "2024-09-06T19:34:34.601136Z", - "iopub.status.idle": "2024-09-06T19:34:34.604946Z", - "shell.execute_reply": "2024-09-06T19:34:34.604479Z" + "iopub.execute_input": "2024-09-26T14:48:35.271336Z", + "iopub.status.busy": "2024-09-26T14:48:35.270995Z", + "iopub.status.idle": "2024-09-26T14:48:35.274864Z", + "shell.execute_reply": "2024-09-26T14:48:35.274455Z" }, "nbsphinx": "hidden" }, @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.607009Z", - "iopub.status.busy": "2024-09-06T19:34:34.606678Z", - "iopub.status.idle": "2024-09-06T19:34:34.615441Z", - "shell.execute_reply": "2024-09-06T19:34:34.614962Z" + "iopub.execute_input": "2024-09-26T14:48:35.276663Z", + "iopub.status.busy": "2024-09-26T14:48:35.276341Z", + "iopub.status.idle": "2024-09-26T14:48:35.285167Z", + "shell.execute_reply": "2024-09-26T14:48:35.284719Z" }, "nbsphinx": "hidden" }, @@ -681,10 +681,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.617637Z", - "iopub.status.busy": "2024-09-06T19:34:34.617189Z", - "iopub.status.idle": "2024-09-06T19:34:34.644027Z", - "shell.execute_reply": "2024-09-06T19:34:34.643475Z" + "iopub.execute_input": "2024-09-26T14:48:35.286904Z", + "iopub.status.busy": "2024-09-26T14:48:35.286584Z", + "iopub.status.idle": "2024-09-26T14:48:35.314453Z", + "shell.execute_reply": "2024-09-26T14:48:35.313973Z" } }, "outputs": [], @@ -721,10 +721,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.646190Z", - "iopub.status.busy": "2024-09-06T19:34:34.645869Z", - "iopub.status.idle": "2024-09-06T19:35:07.856682Z", - "shell.execute_reply": "2024-09-06T19:35:07.856077Z" + "iopub.execute_input": "2024-09-26T14:48:35.316272Z", + "iopub.status.busy": "2024-09-26T14:48:35.315932Z", + "iopub.status.idle": "2024-09-26T14:49:09.563750Z", + "shell.execute_reply": "2024-09-26T14:49:09.563113Z" } }, "outputs": [ @@ -740,21 +740,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.923\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.965\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.597\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.763\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60b6605a27b343f3a046b38e2ee92eb3", + "model_id": "8c99cd03c2204dd69d220e1911ef407b", "version_major": 2, "version_minor": 0 }, @@ -775,7 +775,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "328179309f4646028e9f8909eefb6c74", + "model_id": "ccc0d279330845b8b34f60a57e76743f", "version_major": 2, "version_minor": 0 }, @@ -798,21 +798,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.922\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.062\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.912\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.901\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "958c94ac86804e8fbd31685a6f87d389", + "model_id": "c01e10af9cd04c4c90430d0afbaa6da0", "version_major": 2, "version_minor": 0 }, @@ -833,7 +833,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "593399f7ed16479cabf5d6887e2046b5", + "model_id": "60160531292f49f6912a5e7fa5c1cd4a", "version_major": 2, "version_minor": 0 }, @@ -856,21 +856,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.879\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.009\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.556\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.800\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a3d18201bb14d5c9e73af43adbe2cd8", + "model_id": "e20ec2fb456e4bb9bfb446110e53d341", "version_major": 2, "version_minor": 0 }, @@ -891,7 +891,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cef86182d7ef449481f59dfea70aa34a", + "model_id": "9c97151f1d7f4a49a3e2278cddb3c604", "version_major": 2, "version_minor": 0 }, @@ -970,10 +970,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:07.859270Z", - "iopub.status.busy": "2024-09-06T19:35:07.859022Z", - "iopub.status.idle": "2024-09-06T19:35:07.875302Z", - "shell.execute_reply": "2024-09-06T19:35:07.874880Z" + "iopub.execute_input": "2024-09-26T14:49:09.565771Z", + "iopub.status.busy": "2024-09-26T14:49:09.565547Z", + "iopub.status.idle": "2024-09-26T14:49:09.582928Z", + "shell.execute_reply": "2024-09-26T14:49:09.582459Z" } }, "outputs": [], @@ -998,10 +998,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:07.877195Z", - "iopub.status.busy": "2024-09-06T19:35:07.877017Z", - "iopub.status.idle": "2024-09-06T19:35:08.338418Z", - "shell.execute_reply": "2024-09-06T19:35:08.337844Z" + "iopub.execute_input": "2024-09-26T14:49:09.584801Z", + "iopub.status.busy": "2024-09-26T14:49:09.584617Z", + "iopub.status.idle": "2024-09-26T14:49:10.073024Z", + "shell.execute_reply": "2024-09-26T14:49:10.072472Z" } }, "outputs": [], @@ -1021,10 +1021,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:08.340738Z", - "iopub.status.busy": "2024-09-06T19:35:08.340554Z", - "iopub.status.idle": "2024-09-06T19:36:59.451053Z", - "shell.execute_reply": "2024-09-06T19:36:59.450444Z" + "iopub.execute_input": "2024-09-26T14:49:10.074957Z", + "iopub.status.busy": "2024-09-26T14:49:10.074773Z", + "iopub.status.idle": "2024-09-26T14:51:03.811106Z", + "shell.execute_reply": "2024-09-26T14:51:03.810393Z" } }, "outputs": [ @@ -1063,7 +1063,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8c0903ec57a4db09eef7c66d76ad798", + "model_id": "48ba164541954d109266e55290018b2b", "version_major": 2, "version_minor": 0 }, @@ -1109,10 +1109,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.453745Z", - "iopub.status.busy": "2024-09-06T19:36:59.453098Z", - "iopub.status.idle": "2024-09-06T19:36:59.910431Z", - "shell.execute_reply": "2024-09-06T19:36:59.909867Z" + "iopub.execute_input": "2024-09-26T14:51:03.813379Z", + "iopub.status.busy": "2024-09-26T14:51:03.812973Z", + "iopub.status.idle": "2024-09-26T14:51:04.287434Z", + "shell.execute_reply": "2024-09-26T14:51:04.286542Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.913045Z", - "iopub.status.busy": "2024-09-06T19:36:59.912484Z", - "iopub.status.idle": "2024-09-06T19:36:59.974160Z", - "shell.execute_reply": "2024-09-06T19:36:59.973682Z" + "iopub.execute_input": "2024-09-26T14:51:04.289772Z", + "iopub.status.busy": "2024-09-26T14:51:04.289550Z", + "iopub.status.idle": "2024-09-26T14:51:04.352405Z", + "shell.execute_reply": "2024-09-26T14:51:04.351866Z" } }, "outputs": [ @@ -1365,10 +1365,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.976368Z", - "iopub.status.busy": "2024-09-06T19:36:59.976019Z", - "iopub.status.idle": "2024-09-06T19:36:59.984805Z", - "shell.execute_reply": "2024-09-06T19:36:59.984360Z" + "iopub.execute_input": "2024-09-26T14:51:04.354105Z", + "iopub.status.busy": "2024-09-26T14:51:04.353929Z", + "iopub.status.idle": "2024-09-26T14:51:04.362590Z", + "shell.execute_reply": "2024-09-26T14:51:04.362148Z" } }, "outputs": [ @@ -1498,10 +1498,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.986926Z", - "iopub.status.busy": "2024-09-06T19:36:59.986597Z", - "iopub.status.idle": "2024-09-06T19:36:59.991039Z", - "shell.execute_reply": "2024-09-06T19:36:59.990559Z" + "iopub.execute_input": "2024-09-26T14:51:04.364304Z", + "iopub.status.busy": "2024-09-26T14:51:04.364127Z", + "iopub.status.idle": "2024-09-26T14:51:04.368705Z", + "shell.execute_reply": "2024-09-26T14:51:04.368265Z" }, "nbsphinx": "hidden" }, @@ -1547,10 +1547,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.992932Z", - "iopub.status.busy": "2024-09-06T19:36:59.992715Z", - "iopub.status.idle": "2024-09-06T19:37:00.505081Z", - "shell.execute_reply": "2024-09-06T19:37:00.504451Z" + "iopub.execute_input": "2024-09-26T14:51:04.370241Z", + "iopub.status.busy": "2024-09-26T14:51:04.370067Z", + "iopub.status.idle": "2024-09-26T14:51:04.889724Z", + "shell.execute_reply": "2024-09-26T14:51:04.889048Z" } }, "outputs": [ @@ -1585,10 +1585,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.507663Z", - "iopub.status.busy": "2024-09-06T19:37:00.507293Z", - "iopub.status.idle": "2024-09-06T19:37:00.516488Z", - "shell.execute_reply": "2024-09-06T19:37:00.515888Z" + "iopub.execute_input": "2024-09-26T14:51:04.892026Z", + "iopub.status.busy": "2024-09-26T14:51:04.891753Z", + "iopub.status.idle": "2024-09-26T14:51:04.901156Z", + "shell.execute_reply": "2024-09-26T14:51:04.900641Z" } }, "outputs": [ @@ -1755,10 +1755,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.518970Z", - "iopub.status.busy": "2024-09-06T19:37:00.518520Z", - "iopub.status.idle": "2024-09-06T19:37:00.525985Z", - "shell.execute_reply": "2024-09-06T19:37:00.525525Z" + "iopub.execute_input": "2024-09-26T14:51:04.903329Z", + "iopub.status.busy": "2024-09-26T14:51:04.902914Z", + "iopub.status.idle": "2024-09-26T14:51:04.911571Z", + "shell.execute_reply": "2024-09-26T14:51:04.910975Z" }, "nbsphinx": "hidden" }, @@ -1834,10 +1834,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.528061Z", - "iopub.status.busy": "2024-09-06T19:37:00.527749Z", - "iopub.status.idle": "2024-09-06T19:37:00.996315Z", - "shell.execute_reply": "2024-09-06T19:37:00.995664Z" + "iopub.execute_input": "2024-09-26T14:51:04.913701Z", + "iopub.status.busy": "2024-09-26T14:51:04.913094Z", + "iopub.status.idle": "2024-09-26T14:51:05.388394Z", + "shell.execute_reply": "2024-09-26T14:51:05.387783Z" } }, "outputs": [ @@ -1874,10 +1874,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.998663Z", - "iopub.status.busy": "2024-09-06T19:37:00.998226Z", - "iopub.status.idle": "2024-09-06T19:37:01.014613Z", - "shell.execute_reply": "2024-09-06T19:37:01.014119Z" + "iopub.execute_input": "2024-09-26T14:51:05.390453Z", + "iopub.status.busy": "2024-09-26T14:51:05.390012Z", + "iopub.status.idle": "2024-09-26T14:51:05.406956Z", + "shell.execute_reply": "2024-09-26T14:51:05.406349Z" } }, "outputs": [ @@ -2034,10 +2034,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.016951Z", - "iopub.status.busy": "2024-09-06T19:37:01.016496Z", - "iopub.status.idle": "2024-09-06T19:37:01.022189Z", - "shell.execute_reply": "2024-09-06T19:37:01.021616Z" + "iopub.execute_input": "2024-09-26T14:51:05.408881Z", + "iopub.status.busy": "2024-09-26T14:51:05.408597Z", + "iopub.status.idle": "2024-09-26T14:51:05.415372Z", + "shell.execute_reply": "2024-09-26T14:51:05.414805Z" }, "nbsphinx": "hidden" }, @@ -2082,10 +2082,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.024335Z", - "iopub.status.busy": "2024-09-06T19:37:01.024003Z", - "iopub.status.idle": "2024-09-06T19:37:01.818216Z", - "shell.execute_reply": "2024-09-06T19:37:01.817601Z" + "iopub.execute_input": "2024-09-26T14:51:05.417178Z", + "iopub.status.busy": "2024-09-26T14:51:05.416863Z", + "iopub.status.idle": "2024-09-26T14:51:06.140962Z", + "shell.execute_reply": "2024-09-26T14:51:06.140485Z" } }, "outputs": [ @@ -2167,10 +2167,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.821086Z", - "iopub.status.busy": "2024-09-06T19:37:01.820573Z", - "iopub.status.idle": "2024-09-06T19:37:01.831141Z", - "shell.execute_reply": "2024-09-06T19:37:01.830605Z" + "iopub.execute_input": "2024-09-26T14:51:06.143001Z", + "iopub.status.busy": "2024-09-26T14:51:06.142640Z", + "iopub.status.idle": "2024-09-26T14:51:06.151972Z", + "shell.execute_reply": "2024-09-26T14:51:06.151393Z" } }, "outputs": [ @@ -2298,10 +2298,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.833972Z", - "iopub.status.busy": "2024-09-06T19:37:01.833571Z", - "iopub.status.idle": "2024-09-06T19:37:01.839439Z", - "shell.execute_reply": "2024-09-06T19:37:01.838936Z" + "iopub.execute_input": "2024-09-26T14:51:06.154615Z", + "iopub.status.busy": "2024-09-26T14:51:06.153791Z", + "iopub.status.idle": "2024-09-26T14:51:06.159002Z", + "shell.execute_reply": "2024-09-26T14:51:06.158561Z" }, "nbsphinx": "hidden" }, @@ -2338,10 +2338,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.841836Z", - "iopub.status.busy": "2024-09-06T19:37:01.841454Z", - "iopub.status.idle": "2024-09-06T19:37:02.045788Z", - "shell.execute_reply": "2024-09-06T19:37:02.045180Z" + "iopub.execute_input": "2024-09-26T14:51:06.160864Z", + "iopub.status.busy": "2024-09-26T14:51:06.160545Z", + "iopub.status.idle": "2024-09-26T14:51:06.329043Z", + "shell.execute_reply": "2024-09-26T14:51:06.328546Z" } }, "outputs": [ @@ -2383,10 +2383,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.048026Z", - "iopub.status.busy": "2024-09-06T19:37:02.047682Z", - "iopub.status.idle": "2024-09-06T19:37:02.055980Z", - "shell.execute_reply": "2024-09-06T19:37:02.055509Z" + "iopub.execute_input": "2024-09-26T14:51:06.331161Z", + "iopub.status.busy": "2024-09-26T14:51:06.330733Z", + "iopub.status.idle": "2024-09-26T14:51:06.339030Z", + "shell.execute_reply": "2024-09-26T14:51:06.338452Z" } }, "outputs": [ @@ -2411,47 +2411,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "" ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2472,10 +2472,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.058027Z", - "iopub.status.busy": "2024-09-06T19:37:02.057684Z", - "iopub.status.idle": "2024-09-06T19:37:02.256206Z", - "shell.execute_reply": "2024-09-06T19:37:02.255652Z" + "iopub.execute_input": "2024-09-26T14:51:06.340715Z", + "iopub.status.busy": "2024-09-26T14:51:06.340434Z", + "iopub.status.idle": "2024-09-26T14:51:06.519948Z", + "shell.execute_reply": "2024-09-26T14:51:06.519447Z" } }, "outputs": [ @@ -2515,10 +2515,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.258543Z", - "iopub.status.busy": "2024-09-06T19:37:02.258213Z", - "iopub.status.idle": "2024-09-06T19:37:02.262761Z", - "shell.execute_reply": "2024-09-06T19:37:02.262194Z" + "iopub.execute_input": "2024-09-26T14:51:06.522000Z", + "iopub.status.busy": "2024-09-26T14:51:06.521573Z", + "iopub.status.idle": "2024-09-26T14:51:06.526439Z", + "shell.execute_reply": "2024-09-26T14:51:06.525860Z" }, "nbsphinx": "hidden" }, @@ -2550,12 +2550,95 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "024f58b175f14cdca925ad2ec59e5f75": { + "01a52525efc245dbbd179fb0a46c9b73": { + "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": "" + } + }, + "047ffcbda41946e19a08ad3b61bf841f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_39045bc38857424e8dd68bde536fe596", + "placeholder": "​", + "style": "IPY_MODEL_edbabb45d56b40b7a43858d7ec51dcae", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "0944754e93274569967afe6d608ae5bd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0ec72a2a0065496b8b3b6ff239cb643d": { + "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_22cbf5fe8ee943da90f0599eabdc47e9", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1fd121ab479e40598a964ef1fe640df7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "0fc0d5f5c5fd4be8b001f46a67af29e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2608,25 +2691,33 @@ "width": null } }, - "0445112b7e674aa2a14ea027dc8bc2f8": { + "0fefa1fafea24c19b8f8733109f29d90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_be38efba8d3b4a918b3d7486ef513b34", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a6efe50b16d4bb4b79ecb580262a865", + "tabbable": null, + "tooltip": null, + "value": 2.0 } }, - "06c85507cf49495584b002e6aaa044e8": { + "10f1631d161c449eb8c661cb78376e34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2644,30 +2735,25 @@ "text_color": null } }, - "06fc43e80ab4403ca27ca8d667aca1b3": { + "122c68d343034072a896523a1a6f59da": { "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_4e22cd2b18d5465c8bfc301a968400ec", - "placeholder": "​", - "style": "IPY_MODEL_0445112b7e674aa2a14ea027dc8bc2f8", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "076942f2bb5542e9a9c126f736c4b427": { + "141a122f5b984ea4b8d354438a45f4ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2720,25 +2806,7 @@ "width": null } }, - "07c128e462924e039bd30ffd94caeafe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0a3d18201bb14d5c9e73af43adbe2cd8": { + "157b0b5de92c4dd39861100e0048c0b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2753,57 +2821,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e6d9313a802d4513bc93abf9ffa9fc9b", - "IPY_MODEL_6d3f4ee5439044088f65368ef798b3b6", - "IPY_MODEL_351c45295ff8422c8718ee4bdefa510f" + "IPY_MODEL_46affc3b26cb4f05ba2a6b4d66e4a233", + "IPY_MODEL_e504d10254784e0fb8866b5539c1da25", + "IPY_MODEL_5e794a3967d7453396cb620ce1a5277f" ], - "layout": "IPY_MODEL_7c5730df719649e6ae1137849667983e", + "layout": "IPY_MODEL_b707e815ff5841ea8e23fc04fa7ff454", "tabbable": null, "tooltip": null } }, - "0a606b97ecff4d89be8f66714f909ba3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0b9bdabf441e4113805b54ee83d92f75": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_34e238c0c2c24406adb5d978aec7e807", - "placeholder": "​", - "style": "IPY_MODEL_511e7568ac814ea8846c93d586063e8e", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 285192.56 examples/s]" - } - }, - "0c2a412a844140bd80a53f2ac3fc325d": { + "1af334042da749e9a2368c61aae4462f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2856,33 +2883,41 @@ "width": null } }, - "0f5233a082d94dddbdb0503eb9250ed4": { + "1af3d41b10be4701a4e58df18c1a38f8": { "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_024f58b175f14cdca925ad2ec59e5f75", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c0366a87be804af7bcf6f5cd7f11bc3b", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1b540d6d72a748d0ac6d30324dc37e51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0f970e1142174368bfc637a6cd8d6fd5": { + "1ee09afeb3644ffaa25df19feb6d7756": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2897,15 +2932,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_881dec995ffa41c3b5cbe3a2f2955ed3", + "layout": "IPY_MODEL_a199cd7e7b8c46dea72b37e895857807", "placeholder": "​", - "style": "IPY_MODEL_7500e8476ee64140b7338f73ff7b6e53", + "style": "IPY_MODEL_d0d8518db2324e00900b4cc97bf83cba", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": " 10000/10000 [00:00<00:00, 246828.30 examples/s]" } }, - "10a3911ad0ae43f599855a0ad46d4195": { + "1f4aff5948cf4d0186015296605c7196": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2958,7 +2993,41 @@ "width": null } }, - "11645e47817743048239554d8f897a74": { + "1fd121ab479e40598a964ef1fe640df7": { + "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": "" + } + }, + "20c8eedf90bb40d392876c954589ff68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "22cbf5fe8ee943da90f0599eabdc47e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3011,7 +3080,7 @@ "width": null } }, - "118d13a1737e460b986120e1cd8488c6": { + "23438682b36c4a25982847f0236795a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3064,49 +3133,25 @@ "width": null } }, - "11c48316135a461ea55c3d08dc541755": { + "26577822b8d84e4f976ede8bcd034275": { "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d54af3f6955d4b968858d238a0210190", - "max": 9015.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f2fa408e34274722bd36f3791c967fb2", - "tabbable": null, - "tooltip": null, - "value": 9015.0 - } - }, - "13063e602ee246eb9b552b3b781fa85e": { - "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", + "_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 } }, - "1510cd85e1a74691abd66fcc8f87c34c": { + "27d69304db224bf69131404f9ec805dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3159,60 +3204,25 @@ "width": null } }, - "156d3569cd004246a2f548957d78f2bc": { - "model_module": "@jupyter-widgets/base", + "2a62f69b0c2547d89c82d6582f304d32": { + "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 } }, - "15c77a61cbac476e99fb0331858d1d8c": { + "2a6efe50b16d4bb4b79ecb580262a865": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3228,54 +3238,100 @@ "description_width": "" } }, - "1658cd683cbd496c9ff193ba8d7c35ea": { + "2b2f57b523f849bbb599cc612b663ba6": { "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_b8a497f4724b458c8448b91e3ce44d15", - "placeholder": "​", - "style": "IPY_MODEL_3c640d11994b4372834d50a9621d95c5", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:51<00:00, 1138.50it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "16923bdba0af47908931030b52eaedca": { + "2f87fcdad766443d8c4bca6afb07dd5a": { "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 + } + }, + "309f00f07977438688cb2e2a138942ee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "31aedd70a4c245c99b7449fba431ce39": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "37cb8925a1ba4ade99a9002b6b26e8df": { + "model_module": "@jupyter-widgets/controls", + "model_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_06fc43e80ab4403ca27ca8d667aca1b3", - "IPY_MODEL_0f5233a082d94dddbdb0503eb9250ed4", - "IPY_MODEL_8efceb1c08634d06817a3fa57d1a8f06" - ], - "layout": "IPY_MODEL_a9b1da96fea74c509d14483d998a7cf8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86ca6bb1495e4902b7bab635f193c7b5", + "placeholder": "​", + "style": "IPY_MODEL_c8b6019d0f094ef6b63a722264c996a6", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.07it/s]" } }, - "1e5e214067f448fe820c272b4d8b60b6": { + "3814212d72544fd898cb1f84a4da144b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3291,17 +3347,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_929f19e8f15344c999957b2ce7569264", - "max": 40.0, + "layout": "IPY_MODEL_a7d9118a0a624247afaec6d0c70354df", + "max": 9015.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6c95b5f8fda84c369cae7cba5624ccfe", + "style": "IPY_MODEL_01a52525efc245dbbd179fb0a46c9b73", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 9015.0 } }, - "210dbd38b6ce4f9e9f8b810ac64d03bf": { + "38e12e1caf544c0db7e643ca4333c637": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3317,7 +3373,7 @@ "description_width": "" } }, - "23d399d3b46a4f8d82116059129fd43f": { + "39045bc38857424e8dd68bde536fe596": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3370,7 +3426,7 @@ "width": null } }, - "25c0e4e85ebb41299102ad0b3e0880b4": { + "40c57ff589b84713af0069686d77f3b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3423,30 +3479,7 @@ "width": null } }, - "26b545a844a84c278f68d51645f7e371": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ac970746b6684c3ba6bb43eee4014e2b", - "placeholder": "​", - "style": "IPY_MODEL_c77f86be94734e2ba274bf4267c5a824", - "tabbable": null, - "tooltip": null, - "value": " 2/2 [00:00<00:00, 680.45it/s]" - } - }, - "294184439d7d474cbfc6043c1efa9d3d": { + "41752bd19eff4ad7bd7f964db91e4397": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3499,7 +3532,7 @@ "width": null } }, - "29ace475fdbe4624840332dd0e509ed6": { + "43636151bcde46648bb7aaa89ccb8a51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3552,81 +3585,30 @@ "width": null } }, - "2b755b9c572e43a396e62c74fba5329f": { + "438f1faf1add4ca0bf523ed0c6de324e": { "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_11645e47817743048239554d8f897a74", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7c13df622dfc4f04853781143737296f", + "layout": "IPY_MODEL_7a0e1f1ff0db4d01b7ee26a773a3e838", + "placeholder": "​", + "style": "IPY_MODEL_122c68d343034072a896523a1a6f59da", "tabbable": null, "tooltip": null, - "value": 5175617.0 - } - }, - "3273abc0b1474d17ad8e620a0b9cd685": { - "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_a53594c87ee944d2ab253fdeb3aeae5f", - "IPY_MODEL_e38940706f02447996928468d3f523eb", - "IPY_MODEL_e127093a41b442febda97da50e709395" - ], - "layout": "IPY_MODEL_d9bfaf958ae54bdaa41267876482d6af", - "tabbable": null, - "tooltip": null - } - }, - "328179309f4646028e9f8909eefb6c74": { - "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_471bce1ec58f4d4da4ed7ed290449c5f", - "IPY_MODEL_a8fc249e1ca44d4084ed4e8e978d6058", - "IPY_MODEL_f03a0d23baa4409abb0c7271bd76ab8a" - ], - "layout": "IPY_MODEL_bb2fc960507949aea6439fcd3c77de5b", - "tabbable": null, - "tooltip": null + "value": "Downloading readme: 100%" } }, - "33d5aee8319348e485ec3980bc726f23": { + "442d008a589d43729e86f45feea617a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3679,7 +3661,7 @@ "width": null } }, - "34b3d7273cdd4ceca25a11fbb32359ce": { + "44350a751399489a9b7ac94b4aa0544e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3732,60 +3714,33 @@ "width": null } }, - "34e238c0c2c24406adb5d978aec7e807": { - "model_module": "@jupyter-widgets/base", + "4472b003fd7d40f4b054d6725929c2e0": { + "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_6c8fe2efdded46d4846420593a00bcd9", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_acc0b8fed3ec45c2b61e4cbdd87c0d3f", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "351c45295ff8422c8718ee4bdefa510f": { + "466806dd912b4f9da060546451df881b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3800,15 +3755,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8e65bd8055fd45d8999b608481497477", + "layout": "IPY_MODEL_5171002b8b9b40e58bc90a21fd7d4fce", "placeholder": "​", - "style": "IPY_MODEL_82b18a92ec124a52a6892b31409db80e", + "style": "IPY_MODEL_a1491d109915441ca6418a110cd3b606", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.40it/s]" + "value": "100%" } }, - "3c640d11994b4372834d50a9621d95c5": { + "4685982b10924f9aaf548d58e65e7fff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3826,100 +3781,112 @@ "text_color": null } }, - "468f054b84de4a46abae17b5d6030a66": { + "46affc3b26cb4f05ba2a6b4d66e4a233": { "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_49329574ba97425f8b3b12f07b8d53cf", - "IPY_MODEL_2b755b9c572e43a396e62c74fba5329f", - "IPY_MODEL_861644c40333498094e62f8ac990f5a3" - ], - "layout": "IPY_MODEL_0c2a412a844140bd80a53f2ac3fc325d", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1af334042da749e9a2368c61aae4462f", + "placeholder": "​", + "style": "IPY_MODEL_8fbebe50e8c94c68b5af42bcaa77c458", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading data: 100%" } }, - "471bce1ec58f4d4da4ed7ed290449c5f": { + "46d058b0dc32483b92ffc747203adfbb": { "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_d9b3afcffed047abb3d7c9215eacb041", - "placeholder": "​", - "style": "IPY_MODEL_d0dfbf919e5f422689492055a00be836", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "49329574ba97425f8b3b12f07b8d53cf": { + "48ba164541954d109266e55290018b2b": { "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_77caecf6cce84ac885a2c431ec321e76", - "placeholder": "​", - "style": "IPY_MODEL_df804279770c4bbdbba569e996a72047", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cb02675b1ffa460abaf2766e247e9e76", + "IPY_MODEL_f5bdc90f13ac4085a6c87c3f16c1db23", + "IPY_MODEL_8e5227fef7bb4af398c777c479c081d2" + ], + "layout": "IPY_MODEL_442d008a589d43729e86f45feea617a0", "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" + "tooltip": null } }, - "4a900a7bd2894dc2905c92a999845c41": { + "49a7ec98f7764b44b81eb30169e07a30": { "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_78ec8846971b49dc8ac35c9865ab7855", - "placeholder": "​", - "style": "IPY_MODEL_a1e0fb35ccfd46ac9b640c1e3a97a83e", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cf2c49458b23448bbd90dd22355dc406", + "IPY_MODEL_0fefa1fafea24c19b8f8733109f29d90", + "IPY_MODEL_e1863acf55ff485abde9e3dd42efb403" + ], + "layout": "IPY_MODEL_6f45d2ba1fe14a26b1dd8d8cde2b3404", "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 58.88it/s]" + "tooltip": null + } + }, + "49ff26f36c4046ccaa23af5a0e1bd79a": { + "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": "" } }, - "4e22cd2b18d5465c8bfc301a968400ec": { + "4afd56e1be684a4ea49cf9bd94feb822": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3972,7 +3939,74 @@ "width": null } }, - "4e5be3b38c73499194dd5bfcb00e9476": { + "4c60fd9ec0a14e40806e9a0ac5d515de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4d78c4c4290346e58aeff13d1e2863f2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e1f00c2ba7fa42879c2ae82deb9ae36b", + "placeholder": "​", + "style": "IPY_MODEL_10f1631d161c449eb8c661cb78376e34", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 62.27it/s]" + } + }, + "4e00ed69c8994d9990e59ec728ec2a31": { + "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_e5d17f8bb48c489d84f85f8b0e82285e", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2b2f57b523f849bbb599cc612b663ba6", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "4e60241fc8fc4199a601cbd4c617c5e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4025,7 +4059,30 @@ "width": null } }, - "50780f5c92b44525bf711232bc998378": { + "4f30a386d77a45318659e949f29dd3b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b1371d623aa5489ca6e8e84cd59f53d7", + "placeholder": "​", + "style": "IPY_MODEL_31aedd70a4c245c99b7449fba431ce39", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "5171002b8b9b40e58bc90a21fd7d4fce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4078,25 +4135,31 @@ "width": null } }, - "511e7568ac814ea8846c93d586063e8e": { + "517b83c613bb49c9ab0cd319caf77fa4": { "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_438f1faf1add4ca0bf523ed0c6de324e", + "IPY_MODEL_3814212d72544fd898cb1f84a4da144b", + "IPY_MODEL_bf819d4aebc746d595ddad41e330f6dd" + ], + "layout": "IPY_MODEL_959554efc55740f0a1054b9bb02b35ab", + "tabbable": null, + "tooltip": null } }, - "5264eae69a4a44c5af270b7caaa7eeb4": { + "52f3a03aa4994380b8b138695e6993e3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4149,54 +4212,7 @@ "width": null } }, - "58bd914194a245c2b1a963606103a9bd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_65a24ce68aaf46d6b97c06a7d9ffc735", - "placeholder": "​", - "style": "IPY_MODEL_ed24804748ab483289fec871eb4e7ebf", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.43it/s]" - } - }, - "593399f7ed16479cabf5d6887e2046b5": { - "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_9db52ec22ade4fc2a207b214ed54d964", - "IPY_MODEL_7774247051444f258f5cb5a4624a6d83", - "IPY_MODEL_f082a4eab5444b019ea911ae0fb7a92d" - ], - "layout": "IPY_MODEL_8a312c718675404cb5eaf36ae41d943d", - "tabbable": null, - "tooltip": null - } - }, - "5bc3a1038e00432097a539e27f83e00f": { + "5407b52abb3543878bac58e171136ce5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4249,10 +4265,112 @@ "width": null } }, - "5cf6c3b877784057a47d544871ab0987": { + "545ab14d8bdc4758954170fd6a6c7f2e": { "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": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d16d5292876e41bbb948c7172eb1b022", + "placeholder": "​", + "style": "IPY_MODEL_9d21835857024eaca20e6c75bc8f65eb", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "54d86e58cead41e6ba4d5f91406ed1b6": { + "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 + } + }, + "554fe55159064f65be3faeef3e587efa": { + "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_44350a751399489a9b7ac94b4aa0544e", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_68ce90ceb8c5418fa6b01cf5ea89177d", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "5568c1b377ee44e2baadcd2713472be4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", @@ -4267,7 +4385,7 @@ "text_color": null } }, - "5e5e4490daf942669f04f85596a7308d": { + "5a0315cb61bf4e1b9c52127d29ddf405": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4282,15 +4400,104 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_29ace475fdbe4624840332dd0e509ed6", + "layout": "IPY_MODEL_fd18df91f07f4dd2951fcd6f5d643d2e", "placeholder": "​", - "style": "IPY_MODEL_f784e45cbe9248ae9c16491028d6bf8e", + "style": "IPY_MODEL_2a62f69b0c2547d89c82d6582f304d32", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 64.27it/s]" + } + }, + "5a31bc5cecd243f3b3fc5adfc57abc50": { + "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": "" + } + }, + "5e114e4103d94679910f2192af574f94": { + "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_c2007b76905c425eb8c5793d5064dd23", + "IPY_MODEL_eb1eb741913649deae955849ad2ff5e8", + "IPY_MODEL_8bf2df37d8174d2faa0d068cc68a2fef" + ], + "layout": "IPY_MODEL_dd4c58e0fd6948c49326bb0bb920e8c2", + "tabbable": null, + "tooltip": null + } + }, + "5e37b90b12544b039698dff9bf1b1167": { + "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_b832eb74e64e4a219cb1a39e71503899", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6cad31ec6e694b91b3e194e785d6cc13", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "5e794a3967d7453396cb620ce1a5277f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_aee54ae8933b42d09ef6de4b7c8e9cee", + "placeholder": "​", + "style": "IPY_MODEL_919e7f7fa04b447e977f5978a6008575", + "tabbable": null, + "tooltip": null, + "value": " 30.9M/30.9M [00:00<00:00, 48.5MB/s]" } }, - "606a0ff67cfd457c88c691c81de63a4c": { + "5f57350bd59941d3b3be376c1389de24": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4308,7 +4515,7 @@ "text_color": null } }, - "60b6605a27b343f3a046b38e2ee92eb3": { + "60160531292f49f6912a5e7fa5c1cd4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4323,39 +4530,94 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f9f45d26f74148d5aafa521c2e42894d", - "IPY_MODEL_f11e1a00f1c942a080552a095321e730", - "IPY_MODEL_b323c9ad204c424e81ad897a8a41faa8" + "IPY_MODEL_047ffcbda41946e19a08ad3b61bf841f", + "IPY_MODEL_4472b003fd7d40f4b054d6725929c2e0", + "IPY_MODEL_bdf9a01c9a4d4a6186410cdd21003fa3" ], - "layout": "IPY_MODEL_f1c64d058ec34988a144f78b8ce7cbc8", + "layout": "IPY_MODEL_27d69304db224bf69131404f9ec805dd", "tabbable": null, "tooltip": null } }, - "612a34311c13432a923b885221f461b0": { + "60b1a0d2d540411383054bcc09a9902a": { "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 + } + }, + "62dcb057ad134472b2f7163715a51417": { + "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_156d3569cd004246a2f548957d78f2bc", - "placeholder": "​", - "style": "IPY_MODEL_0a606b97ecff4d89be8f66714f909ba3", + "layout": "IPY_MODEL_23438682b36c4a25982847f0236795a6", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a31bc5cecd243f3b3fc5adfc57abc50", "tabbable": null, "tooltip": null, - "value": "Generating train split: 100%" + "value": 5175617.0 + } + }, + "63fd06eb481e40ff939a4af2d7ec11e1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "68ce90ceb8c5418fa6b01cf5ea89177d": { + "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": "" } }, - "62625d830bc54505aa74a2a30ef3af9d": { + "6bad8602531348f08c403c73528818d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4408,25 +4670,7 @@ "width": null } }, - "63223699d1124f63b67f209182bf8e11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "65a24ce68aaf46d6b97c06a7d9ffc735": { + "6c8fe2efdded46d4846420593a00bcd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4479,7 +4723,23 @@ "width": null } }, - "6739176497a24677bed9ce1d499ce111": { + "6cad31ec6e694b91b3e194e785d6cc13": { + "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": "" + } + }, + "6f45d2ba1fe14a26b1dd8d8cde2b3404": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4532,7 +4792,31 @@ "width": null } }, - "67956635d769462da5b0f8ec7ca4575b": { + "77e4c9ec41b8452c8963af0b77b5555f": { + "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_8838da60b00243caa8a1c207f1f8be22", + "IPY_MODEL_b934aa90c70b4cfdae426a9ca50e61a4", + "IPY_MODEL_793b99f043774f62a311dedf0c93aba0" + ], + "layout": "IPY_MODEL_83e0553f47d84eca8ffc9341907dd047", + "tabbable": null, + "tooltip": null + } + }, + "793b99f043774f62a311dedf0c93aba0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4547,15 +4831,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_34b3d7273cdd4ceca25a11fbb32359ce", + "layout": "IPY_MODEL_ce14eb0a9f94406f9670f2a1dc1345c7", "placeholder": "​", - "style": "IPY_MODEL_07c128e462924e039bd30ffd94caeafe", + "style": "IPY_MODEL_aafc8b9a1a3a4506b5483ae08647da91", "tabbable": null, "tooltip": null, - "value": "Map (num_proc=4): 100%" + "value": " 60000/60000 [00:00<00:00, 282979.24 examples/s]" } }, - "69af7b2b232142ffa08b9e4439628311": { + "7a0e1f1ff0db4d01b7ee26a773a3e838": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4608,107 +4892,60 @@ "width": null } }, - "6c5276f1cdeb4d6dafd955e313dfb495": { - "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": "" - } - }, - "6c95b5f8fda84c369cae7cba5624ccfe": { - "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": "" - } - }, - "6d3f4ee5439044088f65368ef798b3b6": { - "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_10a3911ad0ae43f599855a0ad46d4195", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ac4bae02a5884435aa084f8524ec36ab", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "6dd2d74eb1d04d61844ec3c03149c90b": { - "model_module": "@jupyter-widgets/controls", + "7de683e49bbf4f65959e300da53047ed": { + "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": "" - } - }, - "6e84e471535f4aa89081faaaa485d6c3": { - "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_eec22da0f1c144399d3b96c5a790810e", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_747540cae7c946758cd31d80531063d5", - "tabbable": null, - "tooltip": null, - "value": 60000.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6e8d203ea1f24d86a8504e8c8f549098": { + "7ef0c36f65034c0f9d5b161441b6e47c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4723,15 +4960,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6fdd97c1ed34454aa0a60e98a62224d2", + "layout": "IPY_MODEL_c91d42b9d1c342cf8093443f4eaa5204", "placeholder": "​", - "style": "IPY_MODEL_9bec32080a844b95be00f98699eec0a5", + "style": "IPY_MODEL_20c8eedf90bb40d392876c954589ff68", "tabbable": null, "tooltip": null, - "value": " 9.02k/9.02k [00:00<00:00, 1.14MB/s]" + "value": "Generating test split: 100%" } }, - "6f0a00d7d264477684a40569e5e3fb89": { + "83e0553f47d84eca8ffc9341907dd047": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4784,7 +5021,7 @@ "width": null } }, - "6f4b003f65a3475b87ea4dfb49e22177": { + "86ca6bb1495e4902b7bab635f193c7b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4837,76 +5074,123 @@ "width": null } }, - "6fdd97c1ed34454aa0a60e98a62224d2": { - "model_module": "@jupyter-widgets/base", + "8838da60b00243caa8a1c207f1f8be22": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e34ca479b1cd4338a1b034c9bdb6cdde", + "placeholder": "​", + "style": "IPY_MODEL_5f57350bd59941d3b3be376c1389de24", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" + } + }, + "8bf2df37d8174d2faa0d068cc68a2fef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9a129d278f5249b2ba66adbc4c7981b4", + "placeholder": "​", + "style": "IPY_MODEL_cbb874850a0f4b8585830f781d02f6c7", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 6394.51 examples/s]" + } + }, + "8c99cd03c2204dd69d220e1911ef407b": { + "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_4f30a386d77a45318659e949f29dd3b0", + "IPY_MODEL_e0c002bd6e8c4598837703a0b54dfdd7", + "IPY_MODEL_37cb8925a1ba4ade99a9002b6b26e8df" + ], + "layout": "IPY_MODEL_5407b52abb3543878bac58e171136ce5", + "tabbable": null, + "tooltip": null + } + }, + "8e5227fef7bb4af398c777c479c081d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_40c57ff589b84713af0069686d77f3b8", + "placeholder": "​", + "style": "IPY_MODEL_1b540d6d72a748d0ac6d30324dc37e51", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:53<00:00, 1198.28it/s]" } }, - "747540cae7c946758cd31d80531063d5": { + "8f801276525d4e939b2f6a4ae43acb97": { "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_dd7812d3359f4747a8d626fa5a9094fb", + "placeholder": "​", + "style": "IPY_MODEL_26577822b8d84e4f976ede8bcd034275", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 59.80it/s]" } }, - "7500e8476ee64140b7338f73ff7b6e53": { + "8fbebe50e8c94c68b5af42bcaa77c458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4924,33 +5208,25 @@ "text_color": null } }, - "7774247051444f258f5cb5a4624a6d83": { + "919e7f7fa04b447e977f5978a6008575": { "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_23d399d3b46a4f8d82116059129fd43f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_13063e602ee246eb9b552b3b781fa85e", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "77caecf6cce84ac885a2c431ec321e76": { + "959554efc55740f0a1054b9bb02b35ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5003,7 +5279,7 @@ "width": null } }, - "78ec8846971b49dc8ac35c9865ab7855": { + "9a129d278f5249b2ba66adbc4c7981b4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5056,25 +5332,30 @@ "width": null } }, - "7acec2eb1b9b4c74860f49bf17a12246": { + "9b43cbe4d995486aa4d260f3e1778a5b": { "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_ccdd8d1027864d7697480a9f400c528c", + "placeholder": "​", + "style": "IPY_MODEL_2f87fcdad766443d8c4bca6afb07dd5a", + "tabbable": null, + "tooltip": null, + "value": " 5.18M/5.18M [00:00<00:00, 35.9MB/s]" } }, - "7bcf07287e5846bcade12829a0129e5a": { + "9c97151f1d7f4a49a3e2278cddb3c604": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5089,32 +5370,60 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0f970e1142174368bfc637a6cd8d6fd5", - "IPY_MODEL_11c48316135a461ea55c3d08dc541755", - "IPY_MODEL_6e8d203ea1f24d86a8504e8c8f549098" + "IPY_MODEL_fb35e5a84e5942e59aa0f8e4a6d4b045", + "IPY_MODEL_0ec72a2a0065496b8b3b6ff239cb643d", + "IPY_MODEL_4d78c4c4290346e58aeff13d1e2863f2" ], - "layout": "IPY_MODEL_6f4b003f65a3475b87ea4dfb49e22177", + "layout": "IPY_MODEL_e2ddf40e09984deca4602e0c72e8bfd6", "tabbable": null, "tooltip": null } }, - "7c13df622dfc4f04853781143737296f": { + "9d21835857024eaca20e6c75bc8f65eb": { "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 + } + }, + "9d4ba4757b8d48bba46623ad8f4b39a3": { + "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_54d86e58cead41e6ba4d5f91406ed1b6", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_38e12e1caf544c0db7e643ca4333c637", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "7c5730df719649e6ae1137849667983e": { + "a02cbe5a9ae843ff95023851f56408fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5167,7 +5476,25 @@ "width": null } }, - "7d5c3f6e3cdc47378b7a095dc828c708": { + "a1491d109915441ca6418a110cd3b606": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a199cd7e7b8c46dea72b37e895857807": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5220,30 +5547,7 @@ "width": null } }, - "7e624075712f49aeb75f702d9f7850d8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5264eae69a4a44c5af270b7caaa7eeb4", - "placeholder": "​", - "style": "IPY_MODEL_dac3e32ab9a846e79186067c2b27a96c", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "82b18a92ec124a52a6892b31409db80e": { + "a3d204cf9a5a441d9ce00a62738821f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5261,7 +5565,7 @@ "text_color": null } }, - "85965cbbe5ef40678b86b3d3f8e4fc95": { + "a4367ae0bcb046beb0aa7fa55ae59b6d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5314,54 +5618,7 @@ "width": null } }, - "85a6da0e361d4bb78dac486525795dad": { - "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_612a34311c13432a923b885221f461b0", - "IPY_MODEL_c20bdfdc755d478c8d2c59d296af1748", - "IPY_MODEL_0b9bdabf441e4113805b54ee83d92f75" - ], - "layout": "IPY_MODEL_5bc3a1038e00432097a539e27f83e00f", - "tabbable": null, - "tooltip": null - } - }, - "861644c40333498094e62f8ac990f5a3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a77a4e7f5f8140bf8475f4d847910210", - "placeholder": "​", - "style": "IPY_MODEL_5cf6c3b877784057a47d544871ab0987", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 25.9MB/s]" - } - }, - "881dec995ffa41c3b5cbe3a2f2955ed3": { + "a7d9118a0a624247afaec6d0c70354df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5414,7 +5671,7 @@ "width": null } }, - "884d6ce901e24a3797e35af5711b0f35": { + "aafc8b9a1a3a4506b5483ae08647da91": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5432,7 +5689,23 @@ "text_color": null } }, - "8a312c718675404cb5eaf36ae41d943d": { + "acc0b8fed3ec45c2b61e4cbdd87c0d3f": { + "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": "" + } + }, + "aee54ae8933b42d09ef6de4b7c8e9cee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5485,7 +5758,7 @@ "width": null } }, - "8cf5382c91f64f789a1af9c7918f14bb": { + "afdc4d351896438cb7e9760348393ddc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5500,15 +5773,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d5d9319b3ee0495d88f51937931cd00c", + "layout": "IPY_MODEL_6bad8602531348f08c403c73528818d1", "placeholder": "​", - "style": "IPY_MODEL_af8db4b8467844b8be9927dab8c5e3d9", + "style": "IPY_MODEL_0944754e93274569967afe6d608ae5bd", "tabbable": null, "tooltip": null, "value": "100%" } }, - "8e65bd8055fd45d8999b608481497477": { + "b1371d623aa5489ca6e8e84cd59f53d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5551,40 +5824,17 @@ "min_height": null, "min_width": null, "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8efceb1c08634d06817a3fa57d1a8f06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_85965cbbe5ef40678b86b3d3f8e4fc95", - "placeholder": "​", - "style": "IPY_MODEL_d86fc4609ac5440a807e454ed938d58e", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 249921.29 examples/s]" + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "92880a6894cd410ba97664fbbbbe340e": { + "b42c916027f0431eb2b8bccc86357bf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5637,7 +5887,7 @@ "width": null } }, - "929f19e8f15344c999957b2ce7569264": { + "b707e815ff5841ea8e23fc04fa7ff454": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5690,31 +5940,7 @@ "width": null } }, - "958c94ac86804e8fbd31685a6f87d389": { - "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_ac11acf9e07f48d1af94fcdf26f8c615", - "IPY_MODEL_a671667e5adf4b9798a98eda0ac57dc8", - "IPY_MODEL_4a900a7bd2894dc2905c92a999845c41" - ], - "layout": "IPY_MODEL_4e5be3b38c73499194dd5bfcb00e9476", - "tabbable": null, - "tooltip": null - } - }, - "97578dccf99646909cc139834ab78ea9": { + "b826bec48c45487bbcae63716ac684fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5767,133 +5993,7 @@ "width": null } }, - "9bec32080a844b95be00f98699eec0a5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "9db52ec22ade4fc2a207b214ed54d964": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_62625d830bc54505aa74a2a30ef3af9d", - "placeholder": "​", - "style": "IPY_MODEL_afce32644b8041548c6a8fefb3255e26", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "9fcb012408c9489cb4882ad5d8d37ecf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a1e0fb35ccfd46ac9b640c1e3a97a83e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a53594c87ee944d2ab253fdeb3aeae5f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_50780f5c92b44525bf711232bc998378", - "placeholder": "​", - "style": "IPY_MODEL_eefff7211ed94c5b90094ff9520c50b5", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "a671667e5adf4b9798a98eda0ac57dc8": { - "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_1510cd85e1a74691abd66fcc8f87c34c", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_15c77a61cbac476e99fb0331858d1d8c", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a77a4e7f5f8140bf8475f4d847910210": { + "b832eb74e64e4a219cb1a39e71503899": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5946,7 +6046,33 @@ "width": null } }, - "a88f012a925b438fbc901a161c09cf50": { + "b934aa90c70b4cfdae426a9ca50e61a4": { + "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_1f4aff5948cf4d0186015296605c7196", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bf1593492574460ca63450ff1667241f", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "badd7c549d834045856ae809d48d9fa6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5999,33 +6125,30 @@ "width": null } }, - "a8fc249e1ca44d4084ed4e8e978d6058": { + "bdf9a01c9a4d4a6186410cdd21003fa3": { "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_076942f2bb5542e9a9c126f736c4b427", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6c5276f1cdeb4d6dafd955e313dfb495", + "layout": "IPY_MODEL_41752bd19eff4ad7bd7f964db91e4397", + "placeholder": "​", + "style": "IPY_MODEL_60b1a0d2d540411383054bcc09a9902a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 40/40 [00:00<00:00, 58.52it/s]" } }, - "a9b1da96fea74c509d14483d998a7cf8": { + "be38efba8d3b4a918b3d7486ef513b34": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6078,60 +6201,152 @@ "width": null } }, - "a9e701e6d5bf4ec2a9c900edea6104e5": { - "model_module": "@jupyter-widgets/base", + "bf1593492574460ca63450ff1667241f": { + "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": "" + } + }, + "bf819d4aebc746d595ddad41e330f6dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d89cbf3e824f490b81fa19d70eb0784a", + "placeholder": "​", + "style": "IPY_MODEL_63fd06eb481e40ff939a4af2d7ec11e1", + "tabbable": null, + "tooltip": null, + "value": " 9.02k/9.02k [00:00<00:00, 960kB/s]" + } + }, + "c01e10af9cd04c4c90430d0afbaa6da0": { + "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_466806dd912b4f9da060546451df881b", + "IPY_MODEL_5e37b90b12544b039698dff9bf1b1167", + "IPY_MODEL_8f801276525d4e939b2f6a4ae43acb97" + ], + "layout": "IPY_MODEL_ca16aad0772747da8b6cf03b6abc3643", + "tabbable": null, + "tooltip": null + } + }, + "c2007b76905c425eb8c5793d5064dd23": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a4367ae0bcb046beb0aa7fa55ae59b6d", + "placeholder": "​", + "style": "IPY_MODEL_309f00f07977438688cb2e2a138942ee", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "c57636ad3dc54dc0926bb56946b10ab1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b42c916027f0431eb2b8bccc86357bf7", + "placeholder": "​", + "style": "IPY_MODEL_4685982b10924f9aaf548d58e65e7fff", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 54.36it/s]" + } + }, + "c5b4b8f4759d4bac96dbdbe270c10816": { + "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 + } + }, + "c8b6019d0f094ef6b63a722264c996a6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "aba8cf45b54448a59ae5e30586981cc2": { + "c91d42b9d1c342cf8093443f4eaa5204": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6184,7 +6399,7 @@ "width": null } }, - "ac11acf9e07f48d1af94fcdf26f8c615": { + "c970c34254fb445a91f6a8570b2ee0a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6199,31 +6414,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_69af7b2b232142ffa08b9e4439628311", + "layout": "IPY_MODEL_43636151bcde46648bb7aaa89ccb8a51", "placeholder": "​", - "style": "IPY_MODEL_9fcb012408c9489cb4882ad5d8d37ecf", + "style": "IPY_MODEL_4c60fd9ec0a14e40806e9a0ac5d515de", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "ac4bae02a5884435aa084f8524ec36ab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": "Downloading data: 100%" } }, - "ac970746b6684c3ba6bb43eee4014e2b": { + "ca16aad0772747da8b6cf03b6abc3643": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6276,25 +6475,30 @@ "width": null } }, - "af8db4b8467844b8be9927dab8c5e3d9": { + "cb02675b1ffa460abaf2766e247e9e76": { "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_52f3a03aa4994380b8b138695e6993e3", + "placeholder": "​", + "style": "IPY_MODEL_46d058b0dc32483b92ffc747203adfbb", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "afce32644b8041548c6a8fefb3255e26": { + "cbb874850a0f4b8585830f781d02f6c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6312,7 +6516,31 @@ "text_color": null } }, - "b149b2726a33413c8e2fde403bed8e98": { + "ccc0d279330845b8b34f60a57e76743f": { + "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_afdc4d351896438cb7e9760348393ddc", + "IPY_MODEL_9d4ba4757b8d48bba46623ad8f4b39a3", + "IPY_MODEL_5a0315cb61bf4e1b9c52127d29ddf405" + ], + "layout": "IPY_MODEL_e23cc3c422934dabbc7fa7851f6ab787", + "tabbable": null, + "tooltip": null + } + }, + "ccdd8d1027864d7697480a9f400c528c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6365,46 +6593,31 @@ "width": null } }, - "b1625d60d8254709b2fbc8015a483069": { - "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": "" - } - }, - "b323c9ad204c424e81ad897a8a41faa8": { + "cd9cd1e986f74424b60db3510b43826d": { "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_294184439d7d474cbfc6043c1efa9d3d", - "placeholder": "​", - "style": "IPY_MODEL_63223699d1124f63b67f209182bf8e11", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7ef0c36f65034c0f9d5b161441b6e47c", + "IPY_MODEL_554fe55159064f65be3faeef3e587efa", + "IPY_MODEL_1ee09afeb3644ffaa25df19feb6d7756" + ], + "layout": "IPY_MODEL_f8bd324861dd4e2ca40f79d16ca6ca8a", "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.13it/s]" + "tooltip": null } }, - "b8a497f4724b458c8448b91e3ce44d15": { + "ce14eb0a9f94406f9670f2a1dc1345c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6457,31 +6670,48 @@ "width": null } }, - "b8c0903ec57a4db09eef7c66d76ad798": { + "cf2c49458b23448bbd90dd22355dc406": { "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_5e5e4490daf942669f04f85596a7308d", - "IPY_MODEL_c1960242d8a3445da3b5cfd12aa829f9", - "IPY_MODEL_1658cd683cbd496c9ff193ba8d7c35ea" - ], - "layout": "IPY_MODEL_6739176497a24677bed9ce1d499ce111", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0fc0d5f5c5fd4be8b001f46a67af29e6", + "placeholder": "​", + "style": "IPY_MODEL_5568c1b377ee44e2baadcd2713472be4", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Computing checksums: 100%" + } + }, + "d0d8518db2324e00900b4cc97bf83cba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bb2fc960507949aea6439fcd3c77de5b": { + "d16d5292876e41bbb948c7172eb1b022": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6534,7 +6764,7 @@ "width": null } }, - "bcb6b4fdcebe40208119e7b000c67176": { + "d89cbf3e824f490b81fa19d70eb0784a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6587,99 +6817,31 @@ "width": null } }, - "bfcb4b6339d14370bc404a61e757edfd": { - "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_67956635d769462da5b0f8ec7ca4575b", - "IPY_MODEL_6e84e471535f4aa89081faaaa485d6c3", - "IPY_MODEL_f0cb3f6ef1cd478f8be08c7d0285e829" - ], - "layout": "IPY_MODEL_cc31bc3295e7426890cf527d78a13416", - "tabbable": null, - "tooltip": null - } - }, - "c0366a87be804af7bcf6f5cd7f11bc3b": { - "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": "" - } - }, - "c1960242d8a3445da3b5cfd12aa829f9": { - "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_92880a6894cd410ba97664fbbbbe340e", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b1625d60d8254709b2fbc8015a483069", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "c20bdfdc755d478c8d2c59d296af1748": { + "dc9f46273db144d982f466b186d6ea8d": { "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_6f0a00d7d264477684a40569e5e3fb89", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_210dbd38b6ce4f9e9f8b810ac64d03bf", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c970c34254fb445a91f6a8570b2ee0a6", + "IPY_MODEL_62dcb057ad134472b2f7163715a51417", + "IPY_MODEL_9b43cbe4d995486aa4d260f3e1778a5b" + ], + "layout": "IPY_MODEL_badd7c549d834045856ae809d48d9fa6", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "c65df96729f9462e8df514c9d2bab3e8": { + "dd4c58e0fd6948c49326bb0bb920e8c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6732,25 +6894,7 @@ "width": null } }, - "c77f86be94734e2ba274bf4267c5a824": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cc31bc3295e7426890cf527d78a13416": { + "dd7812d3359f4747a8d626fa5a9094fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6803,65 +6947,56 @@ "width": null } }, - "cdf6adba96a64fc5a04910695f01468c": { - "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": "" - } - }, - "cef86182d7ef449481f59dfea70aa34a": { + "e0c002bd6e8c4598837703a0b54dfdd7": { "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_8cf5382c91f64f789a1af9c7918f14bb", - "IPY_MODEL_1e5e214067f448fe820c272b4d8b60b6", - "IPY_MODEL_58bd914194a245c2b1a963606103a9bd" - ], - "layout": "IPY_MODEL_25c0e4e85ebb41299102ad0b3e0880b4", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b826bec48c45487bbcae63716ac684fb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f241c4541bfc4e678ceec3ff46602200", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "d0dfbf919e5f422689492055a00be836": { + "e1863acf55ff485abde9e3dd42efb403": { "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_fc81a44ecf7c4ad1b851ab50894a6d71", + "placeholder": "​", + "style": "IPY_MODEL_c5b4b8f4759d4bac96dbdbe270c10816", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 584.78it/s]" } }, - "d54af3f6955d4b968858d238a0210190": { + "e1f00c2ba7fa42879c2ae82deb9ae36b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6914,7 +7049,31 @@ "width": null } }, - "d5d9319b3ee0495d88f51937931cd00c": { + "e20ec2fb456e4bb9bfb446110e53d341": { + "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_545ab14d8bdc4758954170fd6a6c7f2e", + "IPY_MODEL_4e00ed69c8994d9990e59ec728ec2a31", + "IPY_MODEL_c57636ad3dc54dc0926bb56946b10ab1" + ], + "layout": "IPY_MODEL_141a122f5b984ea4b8d354438a45f4ac", + "tabbable": null, + "tooltip": null + } + }, + "e23cc3c422934dabbc7fa7851f6ab787": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6967,51 +7126,7 @@ "width": null } }, - "d86fc4609ac5440a807e454ed938d58e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d936e9a2111644719473853bc9465d85": { - "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_aba8cf45b54448a59ae5e30586981cc2", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e06e8dd00ef946db9d9676c674e9f1ff", - "tabbable": null, - "tooltip": null, - "value": 2.0 - } - }, - "d9b3afcffed047abb3d7c9215eacb041": { + "e2ddf40e09984deca4602e0c72e8bfd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7064,7 +7179,7 @@ "width": null } }, - "d9bfaf958ae54bdaa41267876482d6af": { + "e34ca479b1cd4338a1b034c9bdb6cdde": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7111,197 +7226,39 @@ "order": null, "overflow": null, "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "da5cdaff84244e95b85f4f6729933e89": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "dac3e32ab9a846e79186067c2b27a96c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "df804279770c4bbdbba569e996a72047": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e06e8dd00ef946db9d9676c674e9f1ff": { - "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": "" - } - }, - "e127093a41b442febda97da50e709395": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7d5c3f6e3cdc47378b7a095dc828c708", - "placeholder": "​", - "style": "IPY_MODEL_7acec2eb1b9b4c74860f49bf17a12246", - "tabbable": null, - "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 85.2MB/s]" - } - }, - "e38940706f02447996928468d3f523eb": { - "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_97578dccf99646909cc139834ab78ea9", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cdf6adba96a64fc5a04910695f01468c", - "tabbable": null, - "tooltip": null, - "value": 30931277.0 + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "e6d9313a802d4513bc93abf9ffa9fc9b": { + "e504d10254784e0fb8866b5539c1da25": { "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_bcb6b4fdcebe40208119e7b000c67176", - "placeholder": "​", - "style": "IPY_MODEL_606a0ff67cfd457c88c691c81de63a4c", + "layout": "IPY_MODEL_4afd56e1be684a4ea49cf9bd94feb822", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1af3d41b10be4701a4e58df18c1a38f8", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "ed24804748ab483289fec871eb4e7ebf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ee5568e238c045b59cf17074e12437c9": { - "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_7e624075712f49aeb75f702d9f7850d8", - "IPY_MODEL_d936e9a2111644719473853bc9465d85", - "IPY_MODEL_26b545a844a84c278f68d51645f7e371" - ], - "layout": "IPY_MODEL_33d5aee8319348e485ec3980bc726f23", - "tabbable": null, - "tooltip": null + "value": 30931277.0 } }, - "eec22da0f1c144399d3b96c5a790810e": { + "e5d17f8bb48c489d84f85f8b0e82285e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7354,94 +7311,83 @@ "width": null } }, - "eefff7211ed94c5b90094ff9520c50b5": { + "ea25312b42fa433294a739c7e0d5c2a9": { "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": "" } }, - "f03a0d23baa4409abb0c7271bd76ab8a": { + "eb1eb741913649deae955849ad2ff5e8": { "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_c65df96729f9462e8df514c9d2bab3e8", - "placeholder": "​", - "style": "IPY_MODEL_06c85507cf49495584b002e6aaa044e8", + "layout": "IPY_MODEL_a02cbe5a9ae843ff95023851f56408fe", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ea25312b42fa433294a739c7e0d5c2a9", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.46it/s]" + "value": 60000.0 } }, - "f082a4eab5444b019ea911ae0fb7a92d": { + "edbabb45d56b40b7a43858d7ec51dcae": { "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_a88f012a925b438fbc901a161c09cf50", - "placeholder": "​", - "style": "IPY_MODEL_f7b0cd88615641199d9599093664c3f3", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.47it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0cb3f6ef1cd478f8be08c7d0285e829": { + "f241c4541bfc4e678ceec3ff46602200": { "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_118d13a1737e460b986120e1cd8488c6", - "placeholder": "​", - "style": "IPY_MODEL_884d6ce901e24a3797e35af5711b0f35", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 5023.35 examples/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f11e1a00f1c942a080552a095321e730": { + "f5bdc90f13ac4085a6c87c3f16c1db23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -7457,17 +7403,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b149b2726a33413c8e2fde403bed8e98", - "max": 40.0, + "layout": "IPY_MODEL_7de683e49bbf4f65959e300da53047ed", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6dd2d74eb1d04d61844ec3c03149c90b", + "style": "IPY_MODEL_49ff26f36c4046ccaa23af5a0e1bd79a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 } }, - "f1c64d058ec34988a144f78b8ce7cbc8": { + "f8bd324861dd4e2ca40f79d16ca6ca8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7520,79 +7466,133 @@ "width": null } }, - "f2fa408e34274722bd36f3791c967fb2": { + "fb35e5a84e5942e59aa0f8e4a6d4b045": { "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_4e60241fc8fc4199a601cbd4c617c5e6", + "placeholder": "​", + "style": "IPY_MODEL_a3d204cf9a5a441d9ce00a62738821f3", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "f784e45cbe9248ae9c16491028d6bf8e": { - "model_module": "@jupyter-widgets/controls", + "fc81a44ecf7c4ad1b851ab50894a6d71": { + "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 } }, - "f7b0cd88615641199d9599093664c3f3": { - "model_module": "@jupyter-widgets/controls", + "fd18df91f07f4dd2951fcd6f5d643d2e": { + "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 - } - }, - "f9f45d26f74148d5aafa521c2e42894d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a9e701e6d5bf4ec2a9c900edea6104e5", - "placeholder": "​", - "style": "IPY_MODEL_da5cdaff84244e95b85f4f6729933e89", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 55a26f513..688f732c4 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": "2024-09-06T19:37:06.951842Z", - "iopub.status.busy": "2024-09-06T19:37:06.951670Z", - "iopub.status.idle": "2024-09-06T19:37:08.104160Z", - "shell.execute_reply": "2024-09-06T19:37:08.103605Z" + "iopub.execute_input": "2024-09-26T14:51:11.092091Z", + "iopub.status.busy": "2024-09-26T14:51:11.091687Z", + "iopub.status.idle": "2024-09-26T14:51:12.301495Z", + "shell.execute_reply": "2024-09-26T14:51:12.300906Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:08.106594Z", - "iopub.status.busy": "2024-09-06T19:37:08.106312Z", - "iopub.status.idle": "2024-09-06T19:37:08.124373Z", - "shell.execute_reply": "2024-09-06T19:37:08.123937Z" + "iopub.execute_input": "2024-09-26T14:51:12.303829Z", + "iopub.status.busy": "2024-09-26T14:51:12.303363Z", + "iopub.status.idle": "2024-09-26T14:51:12.322353Z", + "shell.execute_reply": "2024-09-26T14:51:12.321902Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.126574Z", - "iopub.status.busy": "2024-09-06T19:37:08.126159Z", - "iopub.status.idle": "2024-09-06T19:37:08.148467Z", - "shell.execute_reply": "2024-09-06T19:37:08.148011Z" + "iopub.execute_input": "2024-09-26T14:51:12.324450Z", + "iopub.status.busy": "2024-09-26T14:51:12.324010Z", + "iopub.status.idle": "2024-09-26T14:51:12.348557Z", + "shell.execute_reply": "2024-09-26T14:51:12.348062Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.150542Z", - "iopub.status.busy": "2024-09-06T19:37:08.150195Z", - "iopub.status.idle": "2024-09-06T19:37:08.153510Z", - "shell.execute_reply": "2024-09-06T19:37:08.153043Z" + "iopub.execute_input": "2024-09-26T14:51:12.350597Z", + "iopub.status.busy": "2024-09-26T14:51:12.350164Z", + "iopub.status.idle": "2024-09-26T14:51:12.353712Z", + "shell.execute_reply": "2024-09-26T14:51:12.353237Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.155506Z", - "iopub.status.busy": "2024-09-06T19:37:08.155162Z", - "iopub.status.idle": "2024-09-06T19:37:08.163216Z", - "shell.execute_reply": "2024-09-06T19:37:08.162658Z" + "iopub.execute_input": "2024-09-26T14:51:12.355535Z", + "iopub.status.busy": "2024-09-26T14:51:12.355193Z", + "iopub.status.idle": "2024-09-26T14:51:12.364277Z", + "shell.execute_reply": "2024-09-26T14:51:12.363833Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.165384Z", - "iopub.status.busy": "2024-09-06T19:37:08.164978Z", - "iopub.status.idle": "2024-09-06T19:37:08.167532Z", - "shell.execute_reply": "2024-09-06T19:37:08.167093Z" + "iopub.execute_input": "2024-09-26T14:51:12.366192Z", + "iopub.status.busy": "2024-09-26T14:51:12.365860Z", + "iopub.status.idle": "2024-09-26T14:51:12.368238Z", + "shell.execute_reply": "2024-09-26T14:51:12.367806Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.169550Z", - "iopub.status.busy": "2024-09-06T19:37:08.169205Z", - "iopub.status.idle": "2024-09-06T19:37:11.232996Z", - "shell.execute_reply": "2024-09-06T19:37:11.232340Z" + "iopub.execute_input": "2024-09-26T14:51:12.369910Z", + "iopub.status.busy": "2024-09-26T14:51:12.369584Z", + "iopub.status.idle": "2024-09-26T14:51:15.473892Z", + "shell.execute_reply": "2024-09-26T14:51:15.473328Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:11.235550Z", - "iopub.status.busy": "2024-09-06T19:37:11.235362Z", - "iopub.status.idle": "2024-09-06T19:37:11.244291Z", - "shell.execute_reply": "2024-09-06T19:37:11.243862Z" + "iopub.execute_input": "2024-09-26T14:51:15.476275Z", + "iopub.status.busy": "2024-09-26T14:51:15.475917Z", + "iopub.status.idle": "2024-09-26T14:51:15.485407Z", + "shell.execute_reply": "2024-09-26T14:51:15.484796Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:11.246379Z", - "iopub.status.busy": "2024-09-06T19:37:11.246205Z", - "iopub.status.idle": "2024-09-06T19:37:13.219249Z", - "shell.execute_reply": "2024-09-06T19:37:13.218645Z" + "iopub.execute_input": "2024-09-26T14:51:15.487349Z", + "iopub.status.busy": "2024-09-26T14:51:15.487005Z", + "iopub.status.idle": "2024-09-26T14:51:17.515517Z", + "shell.execute_reply": "2024-09-26T14:51:17.514901Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.221677Z", - "iopub.status.busy": "2024-09-06T19:37:13.221173Z", - "iopub.status.idle": "2024-09-06T19:37:13.240218Z", - "shell.execute_reply": "2024-09-06T19:37:13.239749Z" + "iopub.execute_input": "2024-09-26T14:51:17.517807Z", + "iopub.status.busy": "2024-09-26T14:51:17.517109Z", + "iopub.status.idle": "2024-09-26T14:51:17.536120Z", + "shell.execute_reply": "2024-09-26T14:51:17.535624Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.242381Z", - "iopub.status.busy": "2024-09-06T19:37:13.242042Z", - "iopub.status.idle": "2024-09-06T19:37:13.250225Z", - "shell.execute_reply": "2024-09-06T19:37:13.249765Z" + "iopub.execute_input": "2024-09-26T14:51:17.537976Z", + "iopub.status.busy": "2024-09-26T14:51:17.537611Z", + "iopub.status.idle": "2024-09-26T14:51:17.545869Z", + "shell.execute_reply": "2024-09-26T14:51:17.545319Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.252315Z", - "iopub.status.busy": "2024-09-06T19:37:13.251975Z", - "iopub.status.idle": "2024-09-06T19:37:13.260671Z", - "shell.execute_reply": "2024-09-06T19:37:13.260195Z" + "iopub.execute_input": "2024-09-26T14:51:17.547622Z", + "iopub.status.busy": "2024-09-26T14:51:17.547301Z", + "iopub.status.idle": "2024-09-26T14:51:17.556250Z", + "shell.execute_reply": "2024-09-26T14:51:17.555755Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.262712Z", - "iopub.status.busy": "2024-09-06T19:37:13.262373Z", - "iopub.status.idle": "2024-09-06T19:37:13.270531Z", - "shell.execute_reply": "2024-09-06T19:37:13.269960Z" + "iopub.execute_input": "2024-09-26T14:51:17.557888Z", + "iopub.status.busy": "2024-09-26T14:51:17.557705Z", + "iopub.status.idle": "2024-09-26T14:51:17.565685Z", + "shell.execute_reply": "2024-09-26T14:51:17.565225Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.272557Z", - "iopub.status.busy": "2024-09-06T19:37:13.272379Z", - "iopub.status.idle": "2024-09-06T19:37:13.281035Z", - "shell.execute_reply": "2024-09-06T19:37:13.280557Z" + "iopub.execute_input": "2024-09-26T14:51:17.567291Z", + "iopub.status.busy": "2024-09-26T14:51:17.567107Z", + "iopub.status.idle": "2024-09-26T14:51:17.576362Z", + "shell.execute_reply": "2024-09-26T14:51:17.575909Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.283068Z", - "iopub.status.busy": "2024-09-06T19:37:13.282889Z", - "iopub.status.idle": "2024-09-06T19:37:13.290486Z", - "shell.execute_reply": "2024-09-06T19:37:13.290023Z" + "iopub.execute_input": "2024-09-26T14:51:17.577990Z", + "iopub.status.busy": "2024-09-26T14:51:17.577812Z", + "iopub.status.idle": "2024-09-26T14:51:17.585393Z", + "shell.execute_reply": "2024-09-26T14:51:17.584817Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.292532Z", - "iopub.status.busy": "2024-09-06T19:37:13.292191Z", - "iopub.status.idle": "2024-09-06T19:37:13.299536Z", - "shell.execute_reply": "2024-09-06T19:37:13.298963Z" + "iopub.execute_input": "2024-09-26T14:51:17.587245Z", + "iopub.status.busy": "2024-09-26T14:51:17.586929Z", + "iopub.status.idle": "2024-09-26T14:51:17.594347Z", + "shell.execute_reply": "2024-09-26T14:51:17.593795Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.301807Z", - "iopub.status.busy": "2024-09-06T19:37:13.301492Z", - "iopub.status.idle": "2024-09-06T19:37:13.309949Z", - "shell.execute_reply": "2024-09-06T19:37:13.309476Z" + "iopub.execute_input": "2024-09-26T14:51:17.596172Z", + "iopub.status.busy": "2024-09-26T14:51:17.595784Z", + "iopub.status.idle": "2024-09-26T14:51:17.604165Z", + "shell.execute_reply": "2024-09-26T14:51:17.603720Z" }, "nbsphinx": "hidden" }, @@ -1373,7 +1373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 0357de56a..5b5c4a565 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": "2024-09-06T19:37:16.238148Z", - "iopub.status.busy": "2024-09-06T19:37:16.237968Z", - "iopub.status.idle": "2024-09-06T19:37:19.032647Z", - "shell.execute_reply": "2024-09-06T19:37:19.031997Z" + "iopub.execute_input": "2024-09-26T14:51:20.550084Z", + "iopub.status.busy": "2024-09-26T14:51:20.549919Z", + "iopub.status.idle": "2024-09-26T14:51:23.546779Z", + "shell.execute_reply": "2024-09-26T14:51:23.546140Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:19.035274Z", - "iopub.status.busy": "2024-09-06T19:37:19.034943Z", - "iopub.status.idle": "2024-09-06T19:37:19.038478Z", - "shell.execute_reply": "2024-09-06T19:37:19.037992Z" + "iopub.execute_input": "2024-09-26T14:51:23.549062Z", + "iopub.status.busy": "2024-09-26T14:51:23.548756Z", + "iopub.status.idle": "2024-09-26T14:51:23.551996Z", + "shell.execute_reply": "2024-09-26T14:51:23.551554Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.040624Z", - "iopub.status.busy": "2024-09-06T19:37:19.040295Z", - "iopub.status.idle": "2024-09-06T19:37:19.043522Z", - "shell.execute_reply": "2024-09-06T19:37:19.043021Z" + "iopub.execute_input": "2024-09-26T14:51:23.553571Z", + "iopub.status.busy": "2024-09-26T14:51:23.553396Z", + "iopub.status.idle": "2024-09-26T14:51:23.556530Z", + "shell.execute_reply": "2024-09-26T14:51:23.556072Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.045678Z", - "iopub.status.busy": "2024-09-06T19:37:19.045330Z", - "iopub.status.idle": "2024-09-06T19:37:19.065598Z", - "shell.execute_reply": "2024-09-06T19:37:19.065087Z" + "iopub.execute_input": "2024-09-26T14:51:23.558190Z", + "iopub.status.busy": "2024-09-26T14:51:23.558016Z", + "iopub.status.idle": "2024-09-26T14:51:23.584373Z", + "shell.execute_reply": "2024-09-26T14:51:23.583877Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.067819Z", - "iopub.status.busy": "2024-09-06T19:37:19.067470Z", - "iopub.status.idle": "2024-09-06T19:37:19.071077Z", - "shell.execute_reply": "2024-09-06T19:37:19.070583Z" + "iopub.execute_input": "2024-09-26T14:51:23.586327Z", + "iopub.status.busy": "2024-09-26T14:51:23.585980Z", + "iopub.status.idle": "2024-09-26T14:51:23.589627Z", + "shell.execute_reply": "2024-09-26T14:51:23.589147Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'change_pin'}\n" + "Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'apple_pay_or_google_pay', 'cancel_transfer'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.073199Z", - "iopub.status.busy": "2024-09-06T19:37:19.072859Z", - "iopub.status.idle": "2024-09-06T19:37:19.075873Z", - "shell.execute_reply": "2024-09-06T19:37:19.075346Z" + "iopub.execute_input": "2024-09-26T14:51:23.591183Z", + "iopub.status.busy": "2024-09-26T14:51:23.591009Z", + "iopub.status.idle": "2024-09-26T14:51:23.594239Z", + "shell.execute_reply": "2024-09-26T14:51:23.593788Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.077966Z", - "iopub.status.busy": "2024-09-06T19:37:19.077636Z", - "iopub.status.idle": "2024-09-06T19:37:23.171760Z", - "shell.execute_reply": "2024-09-06T19:37:23.171196Z" + "iopub.execute_input": "2024-09-26T14:51:23.595893Z", + "iopub.status.busy": "2024-09-26T14:51:23.595586Z", + "iopub.status.idle": "2024-09-26T14:51:27.775987Z", + "shell.execute_reply": "2024-09-26T14:51:27.775330Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:23.174471Z", - "iopub.status.busy": "2024-09-06T19:37:23.174274Z", - "iopub.status.idle": "2024-09-06T19:37:24.103567Z", - "shell.execute_reply": "2024-09-06T19:37:24.102969Z" + "iopub.execute_input": "2024-09-26T14:51:27.778341Z", + "iopub.status.busy": "2024-09-26T14:51:27.777966Z", + "iopub.status.idle": "2024-09-26T14:51:28.697834Z", + "shell.execute_reply": "2024-09-26T14:51:28.697228Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:24.107438Z", - "iopub.status.busy": "2024-09-06T19:37:24.106451Z", - "iopub.status.idle": "2024-09-06T19:37:24.110626Z", - "shell.execute_reply": "2024-09-06T19:37:24.110110Z" + "iopub.execute_input": "2024-09-26T14:51:28.700329Z", + "iopub.status.busy": "2024-09-26T14:51:28.699942Z", + "iopub.status.idle": "2024-09-26T14:51:28.702874Z", + "shell.execute_reply": "2024-09-26T14:51:28.702381Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:24.114244Z", - "iopub.status.busy": "2024-09-06T19:37:24.113304Z", - "iopub.status.idle": "2024-09-06T19:37:26.122882Z", - "shell.execute_reply": "2024-09-06T19:37:26.122195Z" + "iopub.execute_input": "2024-09-26T14:51:28.704853Z", + "iopub.status.busy": "2024-09-26T14:51:28.704499Z", + "iopub.status.idle": "2024-09-26T14:51:30.723899Z", + "shell.execute_reply": "2024-09-26T14:51:30.723229Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.126146Z", - "iopub.status.busy": "2024-09-06T19:37:26.125493Z", - "iopub.status.idle": "2024-09-06T19:37:26.149493Z", - "shell.execute_reply": "2024-09-06T19:37:26.148954Z" + "iopub.execute_input": "2024-09-26T14:51:30.727734Z", + "iopub.status.busy": "2024-09-26T14:51:30.726555Z", + "iopub.status.idle": "2024-09-26T14:51:30.752360Z", + "shell.execute_reply": "2024-09-26T14:51:30.751847Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.152122Z", - "iopub.status.busy": "2024-09-06T19:37:26.151750Z", - "iopub.status.idle": "2024-09-06T19:37:26.163613Z", - "shell.execute_reply": "2024-09-06T19:37:26.163031Z" + "iopub.execute_input": "2024-09-26T14:51:30.755440Z", + "iopub.status.busy": "2024-09-26T14:51:30.754576Z", + "iopub.status.idle": "2024-09-26T14:51:30.764760Z", + "shell.execute_reply": "2024-09-26T14:51:30.764347Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.165819Z", - "iopub.status.busy": "2024-09-06T19:37:26.165507Z", - "iopub.status.idle": "2024-09-06T19:37:26.169927Z", - "shell.execute_reply": "2024-09-06T19:37:26.169445Z" + "iopub.execute_input": "2024-09-26T14:51:30.767190Z", + "iopub.status.busy": "2024-09-26T14:51:30.766574Z", + "iopub.status.idle": "2024-09-26T14:51:30.771522Z", + "shell.execute_reply": "2024-09-26T14:51:30.771112Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.171802Z", - "iopub.status.busy": "2024-09-06T19:37:26.171622Z", - "iopub.status.idle": "2024-09-06T19:37:26.178323Z", - "shell.execute_reply": "2024-09-06T19:37:26.177759Z" + "iopub.execute_input": "2024-09-26T14:51:30.773863Z", + "iopub.status.busy": "2024-09-26T14:51:30.773237Z", + "iopub.status.idle": "2024-09-26T14:51:30.780343Z", + "shell.execute_reply": "2024-09-26T14:51:30.779939Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.180430Z", - "iopub.status.busy": "2024-09-06T19:37:26.180102Z", - "iopub.status.idle": "2024-09-06T19:37:26.186371Z", - "shell.execute_reply": "2024-09-06T19:37:26.185807Z" + "iopub.execute_input": "2024-09-26T14:51:30.782231Z", + "iopub.status.busy": "2024-09-26T14:51:30.782055Z", + "iopub.status.idle": "2024-09-26T14:51:30.788970Z", + "shell.execute_reply": "2024-09-26T14:51:30.788375Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.188480Z", - "iopub.status.busy": "2024-09-06T19:37:26.188150Z", - "iopub.status.idle": "2024-09-06T19:37:26.194198Z", - "shell.execute_reply": "2024-09-06T19:37:26.193624Z" + "iopub.execute_input": "2024-09-26T14:51:30.790778Z", + "iopub.status.busy": "2024-09-26T14:51:30.790601Z", + "iopub.status.idle": "2024-09-26T14:51:30.796446Z", + "shell.execute_reply": "2024-09-26T14:51:30.795882Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.196327Z", - "iopub.status.busy": "2024-09-06T19:37:26.195981Z", - "iopub.status.idle": "2024-09-06T19:37:26.204376Z", - "shell.execute_reply": "2024-09-06T19:37:26.203913Z" + "iopub.execute_input": "2024-09-26T14:51:30.798232Z", + "iopub.status.busy": "2024-09-26T14:51:30.797967Z", + "iopub.status.idle": "2024-09-26T14:51:30.806498Z", + "shell.execute_reply": "2024-09-26T14:51:30.805933Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.206432Z", - "iopub.status.busy": "2024-09-06T19:37:26.206091Z", - "iopub.status.idle": "2024-09-06T19:37:26.211539Z", - "shell.execute_reply": "2024-09-06T19:37:26.211070Z" + "iopub.execute_input": "2024-09-26T14:51:30.808353Z", + "iopub.status.busy": "2024-09-26T14:51:30.808081Z", + "iopub.status.idle": "2024-09-26T14:51:30.813342Z", + "shell.execute_reply": "2024-09-26T14:51:30.812825Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.213685Z", - "iopub.status.busy": "2024-09-06T19:37:26.213350Z", - "iopub.status.idle": "2024-09-06T19:37:26.218528Z", - "shell.execute_reply": "2024-09-06T19:37:26.218074Z" + "iopub.execute_input": "2024-09-26T14:51:30.814997Z", + "iopub.status.busy": "2024-09-26T14:51:30.814668Z", + "iopub.status.idle": "2024-09-26T14:51:30.819982Z", + "shell.execute_reply": "2024-09-26T14:51:30.819532Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.220571Z", - "iopub.status.busy": "2024-09-06T19:37:26.220232Z", - "iopub.status.idle": "2024-09-06T19:37:26.223906Z", - "shell.execute_reply": "2024-09-06T19:37:26.223327Z" + "iopub.execute_input": "2024-09-26T14:51:30.821669Z", + "iopub.status.busy": "2024-09-26T14:51:30.821337Z", + "iopub.status.idle": "2024-09-26T14:51:30.824940Z", + "shell.execute_reply": "2024-09-26T14:51:30.824366Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.226160Z", - "iopub.status.busy": "2024-09-06T19:37:26.225819Z", - "iopub.status.idle": "2024-09-06T19:37:26.231140Z", - "shell.execute_reply": "2024-09-06T19:37:26.230573Z" + "iopub.execute_input": "2024-09-26T14:51:30.826780Z", + "iopub.status.busy": "2024-09-26T14:51:30.826459Z", + "iopub.status.idle": "2024-09-26T14:51:30.831493Z", + "shell.execute_reply": "2024-09-26T14:51:30.831041Z" }, "nbsphinx": "hidden" }, @@ -1497,7 +1497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index 0c93ce2cb..9404f1540 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": "2024-09-06T19:37:29.604724Z", - "iopub.status.busy": "2024-09-06T19:37:29.604545Z", - "iopub.status.idle": "2024-09-06T19:37:30.035194Z", - "shell.execute_reply": "2024-09-06T19:37:30.034674Z" + "iopub.execute_input": "2024-09-26T14:51:34.296488Z", + "iopub.status.busy": "2024-09-26T14:51:34.296076Z", + "iopub.status.idle": "2024-09-26T14:51:35.016105Z", + "shell.execute_reply": "2024-09-26T14:51:35.015553Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.037845Z", - "iopub.status.busy": "2024-09-06T19:37:30.037406Z", - "iopub.status.idle": "2024-09-06T19:37:30.168185Z", - "shell.execute_reply": "2024-09-06T19:37:30.167636Z" + "iopub.execute_input": "2024-09-26T14:51:35.018426Z", + "iopub.status.busy": "2024-09-26T14:51:35.017967Z", + "iopub.status.idle": "2024-09-26T14:51:35.151580Z", + "shell.execute_reply": "2024-09-26T14:51:35.151068Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.170587Z", - "iopub.status.busy": "2024-09-06T19:37:30.170087Z", - "iopub.status.idle": "2024-09-06T19:37:30.193350Z", - "shell.execute_reply": "2024-09-06T19:37:30.192776Z" + "iopub.execute_input": "2024-09-26T14:51:35.153697Z", + "iopub.status.busy": "2024-09-26T14:51:35.153277Z", + "iopub.status.idle": "2024-09-26T14:51:35.177588Z", + "shell.execute_reply": "2024-09-26T14:51:35.176982Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.195997Z", - "iopub.status.busy": "2024-09-06T19:37:30.195790Z", - "iopub.status.idle": "2024-09-06T19:37:32.997740Z", - "shell.execute_reply": "2024-09-06T19:37:32.997128Z" + "iopub.execute_input": "2024-09-26T14:51:35.179788Z", + "iopub.status.busy": "2024-09-26T14:51:35.179361Z", + "iopub.status.idle": "2024-09-26T14:51:37.765581Z", + "shell.execute_reply": "2024-09-26T14:51:37.764993Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 523 issues found in the dataset.\n" + "Audit complete. 524 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356958\n", - " 362\n", + " 0.356924\n", + " 363\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619565\n", + " 0.619581\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356958 362\n", - "3 near_duplicate 0.619565 108\n", + "2 outlier 0.356924 363\n", + "3 near_duplicate 0.619581 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651838 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:33.000426Z", - "iopub.status.busy": "2024-09-06T19:37:32.999838Z", - "iopub.status.idle": "2024-09-06T19:37:42.839981Z", - "shell.execute_reply": "2024-09-06T19:37:42.839475Z" + "iopub.execute_input": "2024-09-26T14:51:37.767993Z", + "iopub.status.busy": "2024-09-26T14:51:37.767425Z", + "iopub.status.idle": "2024-09-26T14:51:46.526023Z", + "shell.execute_reply": "2024-09-26T14:51:46.525421Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:42.842458Z", - "iopub.status.busy": "2024-09-06T19:37:42.842052Z", - "iopub.status.idle": "2024-09-06T19:37:43.014469Z", - "shell.execute_reply": "2024-09-06T19:37:43.013871Z" + "iopub.execute_input": "2024-09-26T14:51:46.528043Z", + "iopub.status.busy": "2024-09-26T14:51:46.527681Z", + "iopub.status.idle": "2024-09-26T14:51:46.730683Z", + "shell.execute_reply": "2024-09-26T14:51:46.730045Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:43.016817Z", - "iopub.status.busy": "2024-09-06T19:37:43.016641Z", - "iopub.status.idle": "2024-09-06T19:37:44.396004Z", - "shell.execute_reply": "2024-09-06T19:37:44.395431Z" + "iopub.execute_input": "2024-09-26T14:51:46.732793Z", + "iopub.status.busy": "2024-09-26T14:51:46.732448Z", + "iopub.status.idle": "2024-09-26T14:51:48.255623Z", + "shell.execute_reply": "2024-09-26T14:51:48.255118Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.398298Z", - "iopub.status.busy": "2024-09-06T19:37:44.397931Z", - "iopub.status.idle": "2024-09-06T19:37:44.810929Z", - "shell.execute_reply": "2024-09-06T19:37:44.810371Z" + "iopub.execute_input": "2024-09-26T14:51:48.257484Z", + "iopub.status.busy": "2024-09-26T14:51:48.257119Z", + "iopub.status.idle": "2024-09-26T14:51:48.773736Z", + "shell.execute_reply": "2024-09-26T14:51:48.773135Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.813440Z", - "iopub.status.busy": "2024-09-06T19:37:44.812940Z", - "iopub.status.idle": "2024-09-06T19:37:44.826271Z", - "shell.execute_reply": "2024-09-06T19:37:44.825842Z" + "iopub.execute_input": "2024-09-26T14:51:48.775864Z", + "iopub.status.busy": "2024-09-26T14:51:48.775323Z", + "iopub.status.idle": "2024-09-26T14:51:48.790103Z", + "shell.execute_reply": "2024-09-26T14:51:48.789626Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.828390Z", - "iopub.status.busy": "2024-09-06T19:37:44.828044Z", - "iopub.status.idle": "2024-09-06T19:37:44.847179Z", - "shell.execute_reply": "2024-09-06T19:37:44.846760Z" + "iopub.execute_input": "2024-09-26T14:51:48.791833Z", + "iopub.status.busy": "2024-09-26T14:51:48.791503Z", + "iopub.status.idle": "2024-09-26T14:51:48.810723Z", + "shell.execute_reply": "2024-09-26T14:51:48.810137Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.849314Z", - "iopub.status.busy": "2024-09-06T19:37:44.848979Z", - "iopub.status.idle": "2024-09-06T19:37:45.077019Z", - "shell.execute_reply": "2024-09-06T19:37:45.076447Z" + "iopub.execute_input": "2024-09-26T14:51:48.812726Z", + "iopub.status.busy": "2024-09-26T14:51:48.812340Z", + "iopub.status.idle": "2024-09-26T14:51:49.055015Z", + "shell.execute_reply": "2024-09-26T14:51:49.054398Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.079688Z", - "iopub.status.busy": "2024-09-06T19:37:45.079281Z", - "iopub.status.idle": "2024-09-06T19:37:45.098946Z", - "shell.execute_reply": "2024-09-06T19:37:45.098466Z" + "iopub.execute_input": "2024-09-26T14:51:49.057480Z", + "iopub.status.busy": "2024-09-26T14:51:49.057052Z", + "iopub.status.idle": "2024-09-26T14:51:49.076673Z", + "shell.execute_reply": "2024-09-26T14:51:49.076195Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.101100Z", - "iopub.status.busy": "2024-09-06T19:37:45.100762Z", - "iopub.status.idle": "2024-09-06T19:37:45.277489Z", - "shell.execute_reply": "2024-09-06T19:37:45.276850Z" + "iopub.execute_input": "2024-09-26T14:51:49.078495Z", + "iopub.status.busy": "2024-09-26T14:51:49.078146Z", + "iopub.status.idle": "2024-09-26T14:51:49.248180Z", + "shell.execute_reply": "2024-09-26T14:51:49.247594Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.279928Z", - "iopub.status.busy": "2024-09-06T19:37:45.279722Z", - "iopub.status.idle": "2024-09-06T19:37:45.290798Z", - "shell.execute_reply": "2024-09-06T19:37:45.290229Z" + "iopub.execute_input": "2024-09-26T14:51:49.250291Z", + "iopub.status.busy": "2024-09-26T14:51:49.249923Z", + "iopub.status.idle": "2024-09-26T14:51:49.260161Z", + "shell.execute_reply": "2024-09-26T14:51:49.259683Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.292867Z", - "iopub.status.busy": "2024-09-06T19:37:45.292672Z", - "iopub.status.idle": "2024-09-06T19:37:45.302178Z", - "shell.execute_reply": "2024-09-06T19:37:45.301745Z" + "iopub.execute_input": "2024-09-26T14:51:49.261950Z", + "iopub.status.busy": "2024-09-26T14:51:49.261604Z", + "iopub.status.idle": "2024-09-26T14:51:49.271258Z", + "shell.execute_reply": "2024-09-26T14:51:49.270689Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.304034Z", - "iopub.status.busy": "2024-09-06T19:37:45.303861Z", - "iopub.status.idle": "2024-09-06T19:37:45.329485Z", - "shell.execute_reply": "2024-09-06T19:37:45.329066Z" + "iopub.execute_input": "2024-09-26T14:51:49.272963Z", + "iopub.status.busy": "2024-09-26T14:51:49.272785Z", + "iopub.status.idle": "2024-09-26T14:51:49.300283Z", + "shell.execute_reply": "2024-09-26T14:51:49.299657Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.331450Z", - "iopub.status.busy": "2024-09-06T19:37:45.331118Z", - "iopub.status.idle": "2024-09-06T19:37:45.333941Z", - "shell.execute_reply": "2024-09-06T19:37:45.333348Z" + "iopub.execute_input": "2024-09-26T14:51:49.302435Z", + "iopub.status.busy": "2024-09-26T14:51:49.302020Z", + "iopub.status.idle": "2024-09-26T14:51:49.304853Z", + "shell.execute_reply": "2024-09-26T14:51:49.304388Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.336081Z", - "iopub.status.busy": "2024-09-06T19:37:45.335742Z", - "iopub.status.idle": "2024-09-06T19:37:45.354797Z", - "shell.execute_reply": "2024-09-06T19:37:45.354315Z" + "iopub.execute_input": "2024-09-26T14:51:49.306559Z", + "iopub.status.busy": "2024-09-26T14:51:49.306373Z", + "iopub.status.idle": "2024-09-26T14:51:49.326211Z", + "shell.execute_reply": "2024-09-26T14:51:49.325620Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.356897Z", - "iopub.status.busy": "2024-09-06T19:37:45.356543Z", - "iopub.status.idle": "2024-09-06T19:37:45.360935Z", - "shell.execute_reply": "2024-09-06T19:37:45.360328Z" + "iopub.execute_input": "2024-09-26T14:51:49.328491Z", + "iopub.status.busy": "2024-09-26T14:51:49.327912Z", + "iopub.status.idle": "2024-09-26T14:51:49.332250Z", + "shell.execute_reply": "2024-09-26T14:51:49.331798Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.363152Z", - "iopub.status.busy": "2024-09-06T19:37:45.362835Z", - "iopub.status.idle": "2024-09-06T19:37:45.390311Z", - "shell.execute_reply": "2024-09-06T19:37:45.389739Z" + "iopub.execute_input": "2024-09-26T14:51:49.334080Z", + "iopub.status.busy": "2024-09-26T14:51:49.333676Z", + "iopub.status.idle": "2024-09-26T14:51:49.363534Z", + "shell.execute_reply": "2024-09-26T14:51:49.362928Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.392321Z", - "iopub.status.busy": "2024-09-06T19:37:45.392005Z", - "iopub.status.idle": "2024-09-06T19:37:45.759141Z", - "shell.execute_reply": "2024-09-06T19:37:45.758581Z" + "iopub.execute_input": "2024-09-26T14:51:49.365331Z", + "iopub.status.busy": "2024-09-26T14:51:49.365032Z", + "iopub.status.idle": "2024-09-26T14:51:49.727339Z", + "shell.execute_reply": "2024-09-26T14:51:49.726743Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.761452Z", - "iopub.status.busy": "2024-09-06T19:37:45.761084Z", - "iopub.status.idle": "2024-09-06T19:37:45.764398Z", - "shell.execute_reply": "2024-09-06T19:37:45.763923Z" + "iopub.execute_input": "2024-09-26T14:51:49.729270Z", + "iopub.status.busy": "2024-09-26T14:51:49.729071Z", + "iopub.status.idle": "2024-09-26T14:51:49.732072Z", + "shell.execute_reply": "2024-09-26T14:51:49.731620Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.766685Z", - "iopub.status.busy": "2024-09-06T19:37:45.766351Z", - "iopub.status.idle": "2024-09-06T19:37:45.779490Z", - "shell.execute_reply": "2024-09-06T19:37:45.779045Z" + "iopub.execute_input": "2024-09-26T14:51:49.733810Z", + "iopub.status.busy": "2024-09-26T14:51:49.733632Z", + "iopub.status.idle": "2024-09-26T14:51:49.747657Z", + "shell.execute_reply": "2024-09-26T14:51:49.747198Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.781428Z", - "iopub.status.busy": "2024-09-06T19:37:45.781250Z", - "iopub.status.idle": "2024-09-06T19:37:45.796041Z", - "shell.execute_reply": "2024-09-06T19:37:45.795601Z" + "iopub.execute_input": "2024-09-26T14:51:49.749243Z", + "iopub.status.busy": "2024-09-26T14:51:49.749065Z", + "iopub.status.idle": "2024-09-26T14:51:49.763193Z", + "shell.execute_reply": "2024-09-26T14:51:49.762714Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.798043Z", - "iopub.status.busy": "2024-09-06T19:37:45.797870Z", - "iopub.status.idle": "2024-09-06T19:37:45.807740Z", - "shell.execute_reply": "2024-09-06T19:37:45.807165Z" + "iopub.execute_input": "2024-09-26T14:51:49.764801Z", + "iopub.status.busy": "2024-09-26T14:51:49.764624Z", + "iopub.status.idle": "2024-09-26T14:51:49.775091Z", + "shell.execute_reply": "2024-09-26T14:51:49.774491Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.809952Z", - "iopub.status.busy": "2024-09-06T19:37:45.809629Z", - "iopub.status.idle": "2024-09-06T19:37:45.818832Z", - "shell.execute_reply": "2024-09-06T19:37:45.818256Z" + "iopub.execute_input": "2024-09-26T14:51:49.777122Z", + "iopub.status.busy": "2024-09-26T14:51:49.776798Z", + "iopub.status.idle": "2024-09-26T14:51:49.786610Z", + "shell.execute_reply": "2024-09-26T14:51:49.786151Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.821154Z", - "iopub.status.busy": "2024-09-06T19:37:45.820691Z", - "iopub.status.idle": "2024-09-06T19:37:45.824900Z", - "shell.execute_reply": "2024-09-06T19:37:45.824317Z" + "iopub.execute_input": "2024-09-26T14:51:49.788278Z", + "iopub.status.busy": "2024-09-26T14:51:49.788101Z", + "iopub.status.idle": "2024-09-26T14:51:49.791818Z", + "shell.execute_reply": "2024-09-26T14:51:49.791364Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.826963Z", - "iopub.status.busy": "2024-09-06T19:37:45.826647Z", - "iopub.status.idle": "2024-09-06T19:37:45.876648Z", - "shell.execute_reply": "2024-09-06T19:37:45.876084Z" + "iopub.execute_input": "2024-09-26T14:51:49.793563Z", + "iopub.status.busy": "2024-09-26T14:51:49.793225Z", + "iopub.status.idle": "2024-09-26T14:51:49.849225Z", + "shell.execute_reply": "2024-09-26T14:51:49.848755Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.878907Z", - "iopub.status.busy": "2024-09-06T19:37:45.878480Z", - "iopub.status.idle": "2024-09-06T19:37:45.884204Z", - "shell.execute_reply": "2024-09-06T19:37:45.883634Z" + "iopub.execute_input": "2024-09-26T14:51:49.851334Z", + "iopub.status.busy": "2024-09-26T14:51:49.850848Z", + "iopub.status.idle": "2024-09-26T14:51:49.856692Z", + "shell.execute_reply": "2024-09-26T14:51:49.856243Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.886291Z", - "iopub.status.busy": "2024-09-06T19:37:45.885973Z", - "iopub.status.idle": "2024-09-06T19:37:45.897008Z", - "shell.execute_reply": "2024-09-06T19:37:45.896438Z" + "iopub.execute_input": "2024-09-26T14:51:49.858413Z", + "iopub.status.busy": "2024-09-26T14:51:49.858094Z", + "iopub.status.idle": "2024-09-26T14:51:49.869805Z", + "shell.execute_reply": "2024-09-26T14:51:49.869218Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.899243Z", - "iopub.status.busy": "2024-09-06T19:37:45.898904Z", - "iopub.status.idle": "2024-09-06T19:37:46.075809Z", - "shell.execute_reply": "2024-09-06T19:37:46.075226Z" + "iopub.execute_input": "2024-09-26T14:51:49.871476Z", + "iopub.status.busy": "2024-09-26T14:51:49.871161Z", + "iopub.status.idle": "2024-09-26T14:51:50.098032Z", + "shell.execute_reply": "2024-09-26T14:51:50.097456Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.078430Z", - "iopub.status.busy": "2024-09-06T19:37:46.077957Z", - "iopub.status.idle": "2024-09-06T19:37:46.085812Z", - "shell.execute_reply": "2024-09-06T19:37:46.085244Z" + "iopub.execute_input": "2024-09-26T14:51:50.099892Z", + "iopub.status.busy": "2024-09-26T14:51:50.099599Z", + "iopub.status.idle": "2024-09-26T14:51:50.107584Z", + "shell.execute_reply": "2024-09-26T14:51:50.107015Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.087762Z", - "iopub.status.busy": "2024-09-06T19:37:46.087589Z", - "iopub.status.idle": "2024-09-06T19:37:46.522443Z", - "shell.execute_reply": "2024-09-06T19:37:46.521749Z" + "iopub.execute_input": "2024-09-26T14:51:50.109288Z", + "iopub.status.busy": "2024-09-26T14:51:50.109111Z", + "iopub.status.idle": "2024-09-26T14:51:50.496608Z", + "shell.execute_reply": "2024-09-26T14:51:50.495787Z" } }, "outputs": [ @@ -3767,7 +3767,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:37:46-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "--2024-09-26 14:51:50-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", "HTTP request sent, awaiting response... " @@ -3783,9 +3783,9 @@ "\r\n", "\r", "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.005s \r\n", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.009s \r\n", "\r\n", - "2024-09-06 19:37:46 (176 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.525178Z", - "iopub.status.busy": "2024-09-06T19:37:46.524748Z", - "iopub.status.idle": "2024-09-06T19:37:48.452276Z", - "shell.execute_reply": "2024-09-06T19:37:48.451758Z" + "iopub.execute_input": "2024-09-26T14:51:50.499275Z", + "iopub.status.busy": "2024-09-26T14:51:50.498755Z", + "iopub.status.idle": "2024-09-26T14:51:52.468119Z", + "shell.execute_reply": "2024-09-26T14:51:52.467505Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:48.454913Z", - "iopub.status.busy": "2024-09-06T19:37:48.454468Z", - "iopub.status.idle": "2024-09-06T19:37:49.092778Z", - "shell.execute_reply": "2024-09-06T19:37:49.092169Z" + "iopub.execute_input": "2024-09-26T14:51:52.470295Z", + "iopub.status.busy": "2024-09-26T14:51:52.470006Z", + "iopub.status.idle": "2024-09-26T14:51:53.135612Z", + "shell.execute_reply": "2024-09-26T14:51:53.134933Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a5793cf283c046f188f735beef4577a5", + "model_id": "819cd513a50348b98c0ff3c8dd72c7bd", "version_major": 2, "version_minor": 0 }, @@ -4008,10 +4008,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.095580Z", - "iopub.status.busy": "2024-09-06T19:37:49.095115Z", - "iopub.status.idle": "2024-09-06T19:37:49.108940Z", - "shell.execute_reply": "2024-09-06T19:37:49.108334Z" + "iopub.execute_input": "2024-09-26T14:51:53.138593Z", + "iopub.status.busy": "2024-09-26T14:51:53.138086Z", + "iopub.status.idle": "2024-09-26T14:51:53.152674Z", + "shell.execute_reply": "2024-09-26T14:51:53.152106Z" } }, "outputs": [ @@ -4257,10 +4257,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.112413Z", - "iopub.status.busy": "2024-09-06T19:37:49.112212Z", - "iopub.status.idle": "2024-09-06T19:37:49.262201Z", - "shell.execute_reply": "2024-09-06T19:37:49.261645Z" + "iopub.execute_input": "2024-09-26T14:51:53.155019Z", + "iopub.status.busy": "2024-09-26T14:51:53.154607Z", + "iopub.status.idle": "2024-09-26T14:51:53.305855Z", + "shell.execute_reply": "2024-09-26T14:51:53.305327Z" } }, "outputs": [ @@ -4325,10 +4325,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.264493Z", - "iopub.status.busy": "2024-09-06T19:37:49.264138Z", - "iopub.status.idle": "2024-09-06T19:37:49.776468Z", - "shell.execute_reply": "2024-09-06T19:37:49.775810Z" + "iopub.execute_input": "2024-09-26T14:51:53.308217Z", + "iopub.status.busy": "2024-09-26T14:51:53.307686Z", + "iopub.status.idle": "2024-09-26T14:51:53.823497Z", + "shell.execute_reply": "2024-09-26T14:51:53.822950Z" }, "nbsphinx": "hidden" }, @@ -4344,7 +4344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e53b81d02870488ca1d70faf1534371f", + "model_id": "ac8a16cb60b04919938bc00b2f1342f7", "version_major": 2, "version_minor": 0 }, @@ -4598,10 +4598,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.778901Z", - "iopub.status.busy": "2024-09-06T19:37:49.778528Z", - "iopub.status.idle": "2024-09-06T19:37:49.924980Z", - "shell.execute_reply": "2024-09-06T19:37:49.924477Z" + "iopub.execute_input": "2024-09-26T14:51:53.825382Z", + "iopub.status.busy": "2024-09-26T14:51:53.825164Z", + "iopub.status.idle": "2024-09-26T14:51:53.978845Z", + "shell.execute_reply": "2024-09-26T14:51:53.978305Z" }, "nbsphinx": "hidden" }, @@ -4648,12 +4648,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "021a50164b8c491ebb069bd57b11ce1a": { + "0cd32d52503a444d88252596c7202d70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4706,30 +4706,7 @@ "width": null } }, - "2cb88e5e7d0f4849b336950480e87a06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5349f02a0bb24786bba46192aa1d90ff", - "placeholder": "​", - "style": "IPY_MODEL_b93c4b8b97f34f0b93a2d334e5065e1b", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "313b234230ce4ce4850b3fa6a5e1b1ee": { + "1e281a0c15b84c80941bc82a97097993": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4782,7 +4759,7 @@ "width": null } }, - "41bdd318b6d1453a8daca74a0776e419": { + "2680a7b149fd4520bb536ef2dfbaa7c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4835,7 +4812,7 @@ "width": null } }, - "5349f02a0bb24786bba46192aa1d90ff": { + "2ea4ee3035ec4460b45119db4b86c88e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4888,105 +4865,25 @@ "width": null } }, - "5664879b48124f5cac1e0a8c43742995": { - "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_da820a1ccd2b42d4a8c12ea0328d1169", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_da8ad4a548a8409389fab7ddc0e601bc", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "6529bc3e5e35424f967dab0385030a5c": { + "305f6e33eec84265b93317eacfe9b6b0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_41bdd318b6d1453a8daca74a0776e419", - "placeholder": "​", - "style": "IPY_MODEL_dfac24cbd04d4a6a9c6a2f3d7e34c87e", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 682.83it/s]" - } - }, - "733b0d114c6e48e6af9ced8acfb5bf3a": { - "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_953f4c82aabd472c9e8dfebdf70939d8", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_86445ca79c764836a406520c67b4b945", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "7b9c39c715b849dbb886ceaeb96e5c35": { - "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_7fb3eb018b9d446294207573ca64cda2", - "placeholder": "​", - "style": "IPY_MODEL_ec3f09ac595d4dadbd0cf34793d57087", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7fb3eb018b9d446294207573ca64cda2": { + "31a345d79d134ece901060efdb94b165": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5039,23 +4936,7 @@ "width": null } }, - "86445ca79c764836a406520c67b4b945": { - "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": "" - } - }, - "953f4c82aabd472c9e8dfebdf70939d8": { + "31b855e014d84579b8434de0e51b2846": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5108,7 +4989,97 @@ "width": null } }, - "9ffc7a8014b64edfad1dd643172601d1": { + "4a9cf0b92b274885a650766f05b77292": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31b855e014d84579b8434de0e51b2846", + "placeholder": "​", + "style": "IPY_MODEL_77f44dc1b8c84ab4aef3e983303eb4a2", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 696.05it/s]" + } + }, + "56f377970b0e41d0a3ab38bfadd0c51b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2680a7b149fd4520bb536ef2dfbaa7c2", + "placeholder": "​", + "style": "IPY_MODEL_a9661f0b7a8d447c8c47dfe0b78f61ef", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "59cfff3ec22848b6944ba0bf323bd24b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5f01f9c656284899b3a91282330101c8": { + "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_829ff0aad29b4270b40cd9499bc93cc7", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_77633068b2ef4937bf213a3297280a11", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "769fa00f56a8417b92d2a48b7c419f62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5123,15 +5094,73 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_021a50164b8c491ebb069bd57b11ce1a", + "layout": "IPY_MODEL_31a345d79d134ece901060efdb94b165", "placeholder": "​", - "style": "IPY_MODEL_da74a2af2dfa4378a23a6009ae2f264c", + "style": "IPY_MODEL_305f6e33eec84265b93317eacfe9b6b0", "tabbable": null, "tooltip": null, - "value": " 200/200 [00:00<00:00, 785.38it/s]" + "value": " 200/200 [00:00<00:00, 735.07it/s]" + } + }, + "77633068b2ef4937bf213a3297280a11": { + "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": "" + } + }, + "77f44dc1b8c84ab4aef3e983303eb4a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "a185cb088b4a4b50933699f586275482": { + "819cd513a50348b98c0ff3c8dd72c7bd": { + "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_92ac731fb51746b9a27792595b65bd81", + "IPY_MODEL_e345abf69b7b4519a731b4da99441fe1", + "IPY_MODEL_769fa00f56a8417b92d2a48b7c419f62" + ], + "layout": "IPY_MODEL_2ea4ee3035ec4460b45119db4b86c88e", + "tabbable": null, + "tooltip": null + } + }, + "829ff0aad29b4270b40cd9499bc93cc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5184,31 +5213,30 @@ "width": null } }, - "a5793cf283c046f188f735beef4577a5": { + "92ac731fb51746b9a27792595b65bd81": { "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_7b9c39c715b849dbb886ceaeb96e5c35", - "IPY_MODEL_5664879b48124f5cac1e0a8c43742995", - "IPY_MODEL_9ffc7a8014b64edfad1dd643172601d1" - ], - "layout": "IPY_MODEL_313b234230ce4ce4850b3fa6a5e1b1ee", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cd32d52503a444d88252596c7202d70", + "placeholder": "​", + "style": "IPY_MODEL_59cfff3ec22848b6944ba0bf323bd24b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "b93c4b8b97f34f0b93a2d334e5065e1b": { + "a9661f0b7a8d447c8c47dfe0b78f61ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5226,25 +5254,31 @@ "text_color": null } }, - "da74a2af2dfa4378a23a6009ae2f264c": { + "ac8a16cb60b04919938bc00b2f1342f7": { "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_56f377970b0e41d0a3ab38bfadd0c51b", + "IPY_MODEL_5f01f9c656284899b3a91282330101c8", + "IPY_MODEL_4a9cf0b92b274885a650766f05b77292" + ], + "layout": "IPY_MODEL_b805213db2034e85bcf8aa102ab8cd3c", + "tabbable": null, + "tooltip": null } }, - "da820a1ccd2b42d4a8c12ea0328d1169": { + "b805213db2034e85bcf8aa102ab8cd3c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5297,7 +5331,7 @@ "width": null } }, - "da8ad4a548a8409389fab7ddc0e601bc": { + "c28c38c2f92e4620b4be9878c38511ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5313,64 +5347,30 @@ "description_width": "" } }, - "dfac24cbd04d4a6a9c6a2f3d7e34c87e": { + "e345abf69b7b4519a731b4da99441fe1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e53b81d02870488ca1d70faf1534371f": { - "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_2cb88e5e7d0f4849b336950480e87a06", - "IPY_MODEL_733b0d114c6e48e6af9ced8acfb5bf3a", - "IPY_MODEL_6529bc3e5e35424f967dab0385030a5c" - ], - "layout": "IPY_MODEL_a185cb088b4a4b50933699f586275482", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e281a0c15b84c80941bc82a97097993", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c28c38c2f92e4620b4be9878c38511ce", "tabbable": null, - "tooltip": null - } - }, - "ec3f09ac595d4dadbd0cf34793d57087": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": 200.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index e932968f7..c8c374ab4 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": "2024-09-06T19:37:53.970574Z", - "iopub.status.busy": "2024-09-06T19:37:53.970388Z", - "iopub.status.idle": "2024-09-06T19:37:55.134808Z", - "shell.execute_reply": "2024-09-06T19:37:55.134157Z" + "iopub.execute_input": "2024-09-26T14:51:59.182546Z", + "iopub.status.busy": "2024-09-26T14:51:59.182366Z", + "iopub.status.idle": "2024-09-26T14:52:00.393643Z", + "shell.execute_reply": "2024-09-26T14:52:00.393076Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:55.137505Z", - "iopub.status.busy": "2024-09-06T19:37:55.137230Z", - "iopub.status.idle": "2024-09-06T19:37:55.140659Z", - "shell.execute_reply": "2024-09-06T19:37:55.140221Z" + "iopub.execute_input": "2024-09-26T14:52:00.395685Z", + "iopub.status.busy": "2024-09-26T14:52:00.395388Z", + "iopub.status.idle": "2024-09-26T14:52:00.398322Z", + "shell.execute_reply": "2024-09-26T14:52:00.397857Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:55.142857Z", - "iopub.status.busy": "2024-09-06T19:37:55.142554Z", - "iopub.status.idle": "2024-09-06T19:37:55.154394Z", - "shell.execute_reply": "2024-09-06T19:37:55.153913Z" + "iopub.execute_input": "2024-09-26T14:52:00.400144Z", + "iopub.status.busy": "2024-09-26T14:52:00.399840Z", + "iopub.status.idle": "2024-09-26T14:52:00.412193Z", + "shell.execute_reply": "2024-09-26T14:52:00.411697Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:55.156367Z", - "iopub.status.busy": "2024-09-06T19:37:55.156193Z", - "iopub.status.idle": "2024-09-06T19:38:03.213180Z", - "shell.execute_reply": "2024-09-06T19:38:03.212490Z" + "iopub.execute_input": "2024-09-26T14:52:00.414113Z", + "iopub.status.busy": "2024-09-26T14:52:00.413741Z", + "iopub.status.idle": "2024-09-26T14:52:05.730687Z", + "shell.execute_reply": "2024-09-26T14:52:05.730191Z" }, "id": "dhTHOg8Pyv5G" }, @@ -3119,7 +3119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index cec52a458..6f20431e7 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": "2024-09-06T19:38:05.442254Z", - "iopub.status.busy": "2024-09-06T19:38:05.441754Z", - "iopub.status.idle": "2024-09-06T19:38:06.608058Z", - "shell.execute_reply": "2024-09-06T19:38:06.607439Z" + "iopub.execute_input": "2024-09-26T14:52:08.034662Z", + "iopub.status.busy": "2024-09-26T14:52:08.034481Z", + "iopub.status.idle": "2024-09-26T14:52:09.304690Z", + "shell.execute_reply": "2024-09-26T14:52:09.304102Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:06.610846Z", - "iopub.status.busy": "2024-09-06T19:38:06.610375Z", - "iopub.status.idle": "2024-09-06T19:38:06.613802Z", - "shell.execute_reply": "2024-09-06T19:38:06.613322Z" + "iopub.execute_input": "2024-09-26T14:52:09.306879Z", + "iopub.status.busy": "2024-09-26T14:52:09.306585Z", + "iopub.status.idle": "2024-09-26T14:52:09.310196Z", + "shell.execute_reply": "2024-09-26T14:52:09.309631Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:06.615798Z", - "iopub.status.busy": "2024-09-06T19:38:06.615518Z", - "iopub.status.idle": "2024-09-06T19:38:09.981363Z", - "shell.execute_reply": "2024-09-06T19:38:09.980664Z" + "iopub.execute_input": "2024-09-26T14:52:09.311928Z", + "iopub.status.busy": "2024-09-26T14:52:09.311543Z", + "iopub.status.idle": "2024-09-26T14:52:12.757719Z", + "shell.execute_reply": "2024-09-26T14:52:12.756901Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:09.984620Z", - "iopub.status.busy": "2024-09-06T19:38:09.983724Z", - "iopub.status.idle": "2024-09-06T19:38:10.027299Z", - "shell.execute_reply": "2024-09-06T19:38:10.026694Z" + "iopub.execute_input": "2024-09-26T14:52:12.760355Z", + "iopub.status.busy": "2024-09-26T14:52:12.759696Z", + "iopub.status.idle": "2024-09-26T14:52:12.813184Z", + "shell.execute_reply": "2024-09-26T14:52:12.812421Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.030074Z", - "iopub.status.busy": "2024-09-06T19:38:10.029673Z", - "iopub.status.idle": "2024-09-06T19:38:10.069413Z", - "shell.execute_reply": "2024-09-06T19:38:10.068633Z" + "iopub.execute_input": "2024-09-26T14:52:12.815571Z", + "iopub.status.busy": "2024-09-26T14:52:12.815173Z", + "iopub.status.idle": "2024-09-26T14:52:12.861989Z", + "shell.execute_reply": "2024-09-26T14:52:12.861319Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.072131Z", - "iopub.status.busy": "2024-09-06T19:38:10.071875Z", - "iopub.status.idle": "2024-09-06T19:38:10.075127Z", - "shell.execute_reply": "2024-09-06T19:38:10.074582Z" + "iopub.execute_input": "2024-09-26T14:52:12.864397Z", + "iopub.status.busy": "2024-09-26T14:52:12.863906Z", + "iopub.status.idle": "2024-09-26T14:52:12.867232Z", + "shell.execute_reply": "2024-09-26T14:52:12.866761Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.077352Z", - "iopub.status.busy": "2024-09-06T19:38:10.077011Z", - "iopub.status.idle": "2024-09-06T19:38:10.079576Z", - "shell.execute_reply": "2024-09-06T19:38:10.079132Z" + "iopub.execute_input": "2024-09-26T14:52:12.868891Z", + "iopub.status.busy": "2024-09-26T14:52:12.868591Z", + "iopub.status.idle": "2024-09-26T14:52:12.871312Z", + "shell.execute_reply": "2024-09-26T14:52:12.870766Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.081910Z", - "iopub.status.busy": "2024-09-06T19:38:10.081719Z", - "iopub.status.idle": "2024-09-06T19:38:10.109741Z", - "shell.execute_reply": "2024-09-06T19:38:10.109183Z" + "iopub.execute_input": "2024-09-26T14:52:12.873230Z", + "iopub.status.busy": "2024-09-26T14:52:12.872884Z", + "iopub.status.idle": "2024-09-26T14:52:12.897801Z", + "shell.execute_reply": "2024-09-26T14:52:12.897165Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10e11ec38b13425280381ff5281c4450", + "model_id": "554f0bffd2414657b0244763906a1e3d", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e2d5adb59434e2081db18c696100263", + "model_id": "d70c6118368a40e3b8c24ac57cc4db26", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.115104Z", - "iopub.status.busy": "2024-09-06T19:38:10.114762Z", - "iopub.status.idle": "2024-09-06T19:38:10.121297Z", - "shell.execute_reply": "2024-09-06T19:38:10.120726Z" + "iopub.execute_input": "2024-09-26T14:52:12.900530Z", + "iopub.status.busy": "2024-09-26T14:52:12.900181Z", + "iopub.status.idle": "2024-09-26T14:52:12.907197Z", + "shell.execute_reply": "2024-09-26T14:52:12.906763Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.123497Z", - "iopub.status.busy": "2024-09-06T19:38:10.123043Z", - "iopub.status.idle": "2024-09-06T19:38:10.126503Z", - "shell.execute_reply": "2024-09-06T19:38:10.126056Z" + "iopub.execute_input": "2024-09-26T14:52:12.908993Z", + "iopub.status.busy": "2024-09-26T14:52:12.908664Z", + "iopub.status.idle": "2024-09-26T14:52:12.911903Z", + "shell.execute_reply": "2024-09-26T14:52:12.911461Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.128505Z", - "iopub.status.busy": "2024-09-06T19:38:10.128204Z", - "iopub.status.idle": "2024-09-06T19:38:10.134549Z", - "shell.execute_reply": "2024-09-06T19:38:10.134003Z" + "iopub.execute_input": "2024-09-26T14:52:12.913714Z", + "iopub.status.busy": "2024-09-26T14:52:12.913385Z", + "iopub.status.idle": "2024-09-26T14:52:12.919520Z", + "shell.execute_reply": "2024-09-26T14:52:12.919085Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.136656Z", - "iopub.status.busy": "2024-09-06T19:38:10.136338Z", - "iopub.status.idle": "2024-09-06T19:38:10.179181Z", - "shell.execute_reply": "2024-09-06T19:38:10.178556Z" + "iopub.execute_input": "2024-09-26T14:52:12.921164Z", + "iopub.status.busy": "2024-09-26T14:52:12.920839Z", + "iopub.status.idle": "2024-09-26T14:52:12.968393Z", + "shell.execute_reply": "2024-09-26T14:52:12.967757Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.181945Z", - "iopub.status.busy": "2024-09-06T19:38:10.181555Z", - "iopub.status.idle": "2024-09-06T19:38:10.218200Z", - "shell.execute_reply": "2024-09-06T19:38:10.217453Z" + "iopub.execute_input": "2024-09-26T14:52:12.970571Z", + "iopub.status.busy": "2024-09-26T14:52:12.970308Z", + "iopub.status.idle": "2024-09-26T14:52:13.022776Z", + "shell.execute_reply": "2024-09-26T14:52:13.022011Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.220958Z", - "iopub.status.busy": "2024-09-06T19:38:10.220569Z", - "iopub.status.idle": "2024-09-06T19:38:10.349381Z", - "shell.execute_reply": "2024-09-06T19:38:10.348725Z" + "iopub.execute_input": "2024-09-26T14:52:13.025203Z", + "iopub.status.busy": "2024-09-26T14:52:13.024937Z", + "iopub.status.idle": "2024-09-26T14:52:13.170260Z", + "shell.execute_reply": "2024-09-26T14:52:13.169652Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.352202Z", - "iopub.status.busy": "2024-09-06T19:38:10.351437Z", - "iopub.status.idle": "2024-09-06T19:38:13.390257Z", - "shell.execute_reply": "2024-09-06T19:38:13.389586Z" + "iopub.execute_input": "2024-09-26T14:52:13.172750Z", + "iopub.status.busy": "2024-09-26T14:52:13.171949Z", + "iopub.status.idle": "2024-09-26T14:52:16.250921Z", + "shell.execute_reply": "2024-09-26T14:52:16.250318Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.392707Z", - "iopub.status.busy": "2024-09-06T19:38:13.392511Z", - "iopub.status.idle": "2024-09-06T19:38:13.450827Z", - "shell.execute_reply": "2024-09-06T19:38:13.450261Z" + "iopub.execute_input": "2024-09-26T14:52:16.253054Z", + "iopub.status.busy": "2024-09-26T14:52:16.252685Z", + "iopub.status.idle": "2024-09-26T14:52:16.313315Z", + "shell.execute_reply": "2024-09-26T14:52:16.312808Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.453108Z", - "iopub.status.busy": "2024-09-06T19:38:13.452688Z", - "iopub.status.idle": "2024-09-06T19:38:13.493414Z", - "shell.execute_reply": "2024-09-06T19:38:13.492941Z" + "iopub.execute_input": "2024-09-26T14:52:16.315165Z", + "iopub.status.busy": "2024-09-26T14:52:16.314831Z", + "iopub.status.idle": "2024-09-26T14:52:16.358568Z", + "shell.execute_reply": "2024-09-26T14:52:16.358096Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "368f0547", + "id": "52d078eb", "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": "dc65d1a9", + "id": "79b5500c", "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": "e31bf904", + "id": "f114fab1", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "0365a86d", + "id": "a6fcaf91", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.495546Z", - "iopub.status.busy": "2024-09-06T19:38:13.495269Z", - "iopub.status.idle": "2024-09-06T19:38:13.502952Z", - "shell.execute_reply": "2024-09-06T19:38:13.502358Z" + "iopub.execute_input": "2024-09-26T14:52:16.360590Z", + "iopub.status.busy": "2024-09-26T14:52:16.360173Z", + "iopub.status.idle": "2024-09-26T14:52:16.368057Z", + "shell.execute_reply": "2024-09-26T14:52:16.367484Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "1c944acb", + "id": "fe87ea59", "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": "c713e4cb", + "id": "6c7bf69f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.504946Z", - "iopub.status.busy": "2024-09-06T19:38:13.504608Z", - "iopub.status.idle": "2024-09-06T19:38:13.523104Z", - "shell.execute_reply": "2024-09-06T19:38:13.522534Z" + "iopub.execute_input": "2024-09-26T14:52:16.369947Z", + "iopub.status.busy": "2024-09-26T14:52:16.369620Z", + "iopub.status.idle": "2024-09-26T14:52:16.389325Z", + "shell.execute_reply": "2024-09-26T14:52:16.388736Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "59184bfc", + "id": "c73832aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.525068Z", - "iopub.status.busy": "2024-09-06T19:38:13.524743Z", - "iopub.status.idle": "2024-09-06T19:38:13.528122Z", - "shell.execute_reply": "2024-09-06T19:38:13.527552Z" + "iopub.execute_input": "2024-09-26T14:52:16.391059Z", + "iopub.status.busy": "2024-09-26T14:52:16.390763Z", + "iopub.status.idle": "2024-09-26T14:52:16.394252Z", + "shell.execute_reply": "2024-09-26T14:52:16.393690Z" } }, "outputs": [ @@ -1617,12 +1617,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a20db80d8ee4c558ba192d544a0f48a": { + "01c2b1694c624296b114bf1d67d63cff": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0b91ac3abe0a43e4b471a93ba3834871": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,7 +1693,25 @@ "width": null } }, - "0e1e83d9b67447b1a76b3a2c668a8439": { + "1c5e90fb88e44d4280704d7ea69107fc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1f603edcaca0493985e74b52602fd4e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1728,25 +1764,46 @@ "width": null } }, - "0ea8c549fffe4418b122c5d1daacdcf9": { + "24e8217d30ae40ec8e547d17a79c5035": { "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": "" + } + }, + "4d41deb2547b43daa3622e6c0f359568": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_853967e0e61449b5b519cbd25c8830fc", + "placeholder": "​", + "style": "IPY_MODEL_70ee3acf89114ab190c8c886bcad952a", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "10e11ec38b13425280381ff5281c4450": { + "554f0bffd2414657b0244763906a1e3d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1761,16 +1818,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_98a30ff8d08f40f5a59fa3959a1bfd7a", - "IPY_MODEL_9e361a1c4f7d49d28575030ed31684b4", - "IPY_MODEL_5d93e4fbfc844d82994983ca2900ac04" + "IPY_MODEL_c8c65a735e994d1792a82e7140824616", + "IPY_MODEL_77dcb3bbbcad4620bbed6ca47c4c44db", + "IPY_MODEL_e241857383f5412490f7baca022471b6" ], - "layout": "IPY_MODEL_4c9d550f7159424fb6452da47b5cb51f", + "layout": "IPY_MODEL_af8c576b995749f49b6a3ff1a3ef7338", "tabbable": null, "tooltip": null } }, - "1989c2b222ef4983ba1d80fd96d80f9d": { + "5569a8600bc64dfebf5774ddc1b6543c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1823,23 +1880,48 @@ "width": null } }, - "278fa17d981b49c5a5afac9215c11437": { + "70745b2f7b374dbb8bf3ac849b0ce45e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1f603edcaca0493985e74b52602fd4e9", + "placeholder": "​", + "style": "IPY_MODEL_01c2b1694c624296b114bf1d67d63cff", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1566266.10it/s]" + } + }, + "70ee3acf89114ab190c8c886bcad952a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4c9d550f7159424fb6452da47b5cb51f": { + "74ffd9bb94c34169aa80dd9d157cd10d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1892,30 +1974,33 @@ "width": null } }, - "5d93e4fbfc844d82994983ca2900ac04": { + "77dcb3bbbcad4620bbed6ca47c4c44db": { "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_0a20db80d8ee4c558ba192d544a0f48a", - "placeholder": "​", - "style": "IPY_MODEL_85eb048e0015452a98d2585ecd3acea6", + "layout": "IPY_MODEL_c8f68f3e3778452dbb68e37345ea22a9", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c7a5057a16474398af969c555e555a5d", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 908211.86it/s]" + "value": 50.0 } }, - "62148dc5598f487787910111c96b2850": { + "853967e0e61449b5b519cbd25c8830fc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1968,30 +2053,7 @@ "width": null } }, - "663eab6313474ab4b43a56ac15375332": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0e1e83d9b67447b1a76b3a2c668a8439", - "placeholder": "​", - "style": "IPY_MODEL_c8b6ee68eda04b79b9d7e8ba44708601", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "71bf8e249c8e494a8293a1368b4cde75": { + "af8c576b995749f49b6a3ff1a3ef7338": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2044,67 +2106,72 @@ "width": null } }, - "7e2d5adb59434e2081db18c696100263": { + "c7a5057a16474398af969c555e555a5d": { "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_663eab6313474ab4b43a56ac15375332", - "IPY_MODEL_cb19cecdebc048139ef9e5b0697091e8", - "IPY_MODEL_b09565a1c786456187dacb880907b06f" - ], - "layout": "IPY_MODEL_62148dc5598f487787910111c96b2850", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "85eb048e0015452a98d2585ecd3acea6": { + "c7fe0e5fdfd74b84af9f7585515c62f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fead4528991a4aafb24e155e68de7bc9", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_24e8217d30ae40ec8e547d17a79c5035", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "8a29c209506d4b1f809b0eee618845ff": { + "c8c65a735e994d1792a82e7140824616": { "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_0b91ac3abe0a43e4b471a93ba3834871", + "placeholder": "​", + "style": "IPY_MODEL_1c5e90fb88e44d4280704d7ea69107fc", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " } }, - "8a7564586c364ea6ab0b8036f15d75de": { + "c8f68f3e3778452dbb68e37345ea22a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2157,56 +2224,31 @@ "width": null } }, - "98a30ff8d08f40f5a59fa3959a1bfd7a": { + "d70c6118368a40e3b8c24ac57cc4db26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_cbbdcb4211b04decb44b6be6dae0e74f", - "placeholder": "​", - "style": "IPY_MODEL_0ea8c549fffe4418b122c5d1daacdcf9", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "9e361a1c4f7d49d28575030ed31684b4": { - "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_8a7564586c364ea6ab0b8036f15d75de", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_278fa17d981b49c5a5afac9215c11437", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4d41deb2547b43daa3622e6c0f359568", + "IPY_MODEL_c7fe0e5fdfd74b84af9f7585515c62f8", + "IPY_MODEL_70745b2f7b374dbb8bf3ac849b0ce45e" + ], + "layout": "IPY_MODEL_74ffd9bb94c34169aa80dd9d157cd10d", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null } }, - "b09565a1c786456187dacb880907b06f": { + "e241857383f5412490f7baca022471b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2221,15 +2263,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_71bf8e249c8e494a8293a1368b4cde75", + "layout": "IPY_MODEL_5569a8600bc64dfebf5774ddc1b6543c", "placeholder": "​", - "style": "IPY_MODEL_8a29c209506d4b1f809b0eee618845ff", + "style": "IPY_MODEL_f6897d47a5094424a94e9a8a0a058c31", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1197722.38it/s]" + "value": " 10000/? [00:00<00:00, 1012407.73it/s]" } }, - "c8b6ee68eda04b79b9d7e8ba44708601": { + "f6897d47a5094424a94e9a8a0a058c31": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2247,33 +2289,7 @@ "text_color": null } }, - "cb19cecdebc048139ef9e5b0697091e8": { - "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_1989c2b222ef4983ba1d80fd96d80f9d", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cd96842c5f86404599e6a57c4439dccf", - "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "cbbdcb4211b04decb44b6be6dae0e74f": { + "fead4528991a4aafb24e155e68de7bc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2325,22 +2341,6 @@ "visibility": null, "width": null } - }, - "cd96842c5f86404599e6a57c4439dccf": { - "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/improving_ml_performance.ipynb b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb index 0126898fa..d59d7a26c 100644 --- a/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:17.966921Z", - "iopub.status.busy": "2024-09-06T19:38:17.966743Z", - "iopub.status.idle": "2024-09-06T19:38:19.153643Z", - "shell.execute_reply": "2024-09-06T19:38:19.153020Z" + "iopub.execute_input": "2024-09-26T14:52:19.810405Z", + "iopub.status.busy": "2024-09-26T14:52:19.810223Z", + "iopub.status.idle": "2024-09-26T14:52:21.040404Z", + "shell.execute_reply": "2024-09-26T14:52:21.039829Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.156468Z", - "iopub.status.busy": "2024-09-06T19:38:19.155927Z", - "iopub.status.idle": "2024-09-06T19:38:19.159820Z", - "shell.execute_reply": "2024-09-06T19:38:19.159280Z" + "iopub.execute_input": "2024-09-26T14:52:21.042734Z", + "iopub.status.busy": "2024-09-26T14:52:21.042166Z", + "iopub.status.idle": "2024-09-26T14:52:21.046124Z", + "shell.execute_reply": "2024-09-26T14:52:21.045639Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.161985Z", - "iopub.status.busy": "2024-09-06T19:38:19.161628Z", - "iopub.status.idle": "2024-09-06T19:38:19.848074Z", - "shell.execute_reply": "2024-09-06T19:38:19.847540Z" + "iopub.execute_input": "2024-09-26T14:52:21.047800Z", + "iopub.status.busy": "2024-09-26T14:52:21.047493Z", + "iopub.status.idle": "2024-09-26T14:52:21.500478Z", + "shell.execute_reply": "2024-09-26T14:52:21.499906Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.850305Z", - "iopub.status.busy": "2024-09-06T19:38:19.849961Z", - "iopub.status.idle": "2024-09-06T19:38:19.855710Z", - "shell.execute_reply": "2024-09-06T19:38:19.855268Z" + "iopub.execute_input": "2024-09-26T14:52:21.502342Z", + "iopub.status.busy": "2024-09-26T14:52:21.502065Z", + "iopub.status.idle": "2024-09-26T14:52:21.509359Z", + "shell.execute_reply": "2024-09-26T14:52:21.508870Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.857664Z", - "iopub.status.busy": "2024-09-06T19:38:19.857483Z", - "iopub.status.idle": "2024-09-06T19:38:19.864510Z", - "shell.execute_reply": "2024-09-06T19:38:19.863928Z" + "iopub.execute_input": "2024-09-26T14:52:21.511294Z", + "iopub.status.busy": "2024-09-26T14:52:21.510958Z", + "iopub.status.idle": "2024-09-26T14:52:21.518230Z", + "shell.execute_reply": "2024-09-26T14:52:21.517794Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.866738Z", - "iopub.status.busy": "2024-09-06T19:38:19.866419Z", - "iopub.status.idle": "2024-09-06T19:38:19.871181Z", - "shell.execute_reply": "2024-09-06T19:38:19.870718Z" + "iopub.execute_input": "2024-09-26T14:52:21.520016Z", + "iopub.status.busy": "2024-09-26T14:52:21.519670Z", + "iopub.status.idle": "2024-09-26T14:52:21.524522Z", + "shell.execute_reply": "2024-09-26T14:52:21.524038Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.873167Z", - "iopub.status.busy": "2024-09-06T19:38:19.872989Z", - "iopub.status.idle": "2024-09-06T19:38:19.879315Z", - "shell.execute_reply": "2024-09-06T19:38:19.878873Z" + "iopub.execute_input": "2024-09-26T14:52:21.526279Z", + "iopub.status.busy": "2024-09-26T14:52:21.525942Z", + "iopub.status.idle": "2024-09-26T14:52:21.531374Z", + "shell.execute_reply": "2024-09-26T14:52:21.530921Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.881299Z", - "iopub.status.busy": "2024-09-06T19:38:19.881109Z", - "iopub.status.idle": "2024-09-06T19:38:19.885448Z", - "shell.execute_reply": "2024-09-06T19:38:19.884866Z" + "iopub.execute_input": "2024-09-26T14:52:21.533093Z", + "iopub.status.busy": "2024-09-26T14:52:21.532754Z", + "iopub.status.idle": "2024-09-26T14:52:21.536654Z", + "shell.execute_reply": "2024-09-26T14:52:21.536203Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.887541Z", - "iopub.status.busy": "2024-09-06T19:38:19.887226Z", - "iopub.status.idle": "2024-09-06T19:38:19.952333Z", - "shell.execute_reply": "2024-09-06T19:38:19.951659Z" + "iopub.execute_input": "2024-09-26T14:52:21.538466Z", + "iopub.status.busy": "2024-09-26T14:52:21.538138Z", + "iopub.status.idle": "2024-09-26T14:52:21.605533Z", + "shell.execute_reply": "2024-09-26T14:52:21.604911Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.955055Z", - "iopub.status.busy": "2024-09-06T19:38:19.954571Z", - "iopub.status.idle": "2024-09-06T19:38:19.965639Z", - "shell.execute_reply": "2024-09-06T19:38:19.965092Z" + "iopub.execute_input": "2024-09-26T14:52:21.608178Z", + "iopub.status.busy": "2024-09-26T14:52:21.607735Z", + "iopub.status.idle": "2024-09-26T14:52:21.620493Z", + "shell.execute_reply": "2024-09-26T14:52:21.619924Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.968612Z", - "iopub.status.busy": "2024-09-06T19:38:19.968081Z", - "iopub.status.idle": "2024-09-06T19:38:19.989523Z", - "shell.execute_reply": "2024-09-06T19:38:19.988990Z" + "iopub.execute_input": "2024-09-26T14:52:21.623400Z", + "iopub.status.busy": "2024-09-26T14:52:21.622546Z", + "iopub.status.idle": "2024-09-26T14:52:21.644716Z", + "shell.execute_reply": "2024-09-26T14:52:21.644193Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.992484Z", - "iopub.status.busy": "2024-09-06T19:38:19.991953Z", - "iopub.status.idle": "2024-09-06T19:38:19.996496Z", - "shell.execute_reply": "2024-09-06T19:38:19.995963Z" + "iopub.execute_input": "2024-09-26T14:52:21.647639Z", + "iopub.status.busy": "2024-09-26T14:52:21.646753Z", + "iopub.status.idle": "2024-09-26T14:52:21.652233Z", + "shell.execute_reply": "2024-09-26T14:52:21.651741Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.000004Z", - "iopub.status.busy": "2024-09-06T19:38:19.999084Z", - "iopub.status.idle": "2024-09-06T19:38:20.005225Z", - "shell.execute_reply": "2024-09-06T19:38:20.004698Z" + "iopub.execute_input": "2024-09-26T14:52:21.654600Z", + "iopub.status.busy": "2024-09-26T14:52:21.654175Z", + "iopub.status.idle": "2024-09-26T14:52:21.659391Z", + "shell.execute_reply": "2024-09-26T14:52:21.658868Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.008748Z", - "iopub.status.busy": "2024-09-06T19:38:20.007824Z", - "iopub.status.idle": "2024-09-06T19:38:20.018446Z", - "shell.execute_reply": "2024-09-06T19:38:20.018010Z" + "iopub.execute_input": "2024-09-26T14:52:21.661608Z", + "iopub.status.busy": "2024-09-26T14:52:21.661407Z", + "iopub.status.idle": "2024-09-26T14:52:21.671252Z", + "shell.execute_reply": "2024-09-26T14:52:21.670825Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.020571Z", - "iopub.status.busy": "2024-09-06T19:38:20.020204Z", - "iopub.status.idle": "2024-09-06T19:38:20.024666Z", - "shell.execute_reply": "2024-09-06T19:38:20.024096Z" + "iopub.execute_input": "2024-09-26T14:52:21.673132Z", + "iopub.status.busy": "2024-09-26T14:52:21.672789Z", + "iopub.status.idle": "2024-09-26T14:52:21.677167Z", + "shell.execute_reply": "2024-09-26T14:52:21.676751Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.026677Z", - "iopub.status.busy": "2024-09-06T19:38:20.026505Z", - "iopub.status.idle": "2024-09-06T19:38:20.138981Z", - "shell.execute_reply": "2024-09-06T19:38:20.138473Z" + "iopub.execute_input": "2024-09-26T14:52:21.678723Z", + "iopub.status.busy": "2024-09-26T14:52:21.678550Z", + "iopub.status.idle": "2024-09-26T14:52:21.827660Z", + "shell.execute_reply": "2024-09-26T14:52:21.827142Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.141251Z", - "iopub.status.busy": "2024-09-06T19:38:20.140804Z", - "iopub.status.idle": "2024-09-06T19:38:20.147269Z", - "shell.execute_reply": "2024-09-06T19:38:20.146678Z" + "iopub.execute_input": "2024-09-26T14:52:21.829459Z", + "iopub.status.busy": "2024-09-26T14:52:21.829100Z", + "iopub.status.idle": "2024-09-26T14:52:21.835627Z", + "shell.execute_reply": "2024-09-26T14:52:21.835049Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.149710Z", - "iopub.status.busy": "2024-09-06T19:38:20.149204Z", - "iopub.status.idle": "2024-09-06T19:38:22.175679Z", - "shell.execute_reply": "2024-09-06T19:38:22.175042Z" + "iopub.execute_input": "2024-09-26T14:52:21.837607Z", + "iopub.status.busy": "2024-09-26T14:52:21.837231Z", + "iopub.status.idle": "2024-09-26T14:52:23.851625Z", + "shell.execute_reply": "2024-09-26T14:52:23.850969Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.179907Z", - "iopub.status.busy": "2024-09-06T19:38:22.178817Z", - "iopub.status.idle": "2024-09-06T19:38:22.193599Z", - "shell.execute_reply": "2024-09-06T19:38:22.193081Z" + "iopub.execute_input": "2024-09-26T14:52:23.853998Z", + "iopub.status.busy": "2024-09-26T14:52:23.853506Z", + "iopub.status.idle": "2024-09-26T14:52:23.867378Z", + "shell.execute_reply": "2024-09-26T14:52:23.866868Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.197201Z", - "iopub.status.busy": "2024-09-06T19:38:22.196240Z", - "iopub.status.idle": "2024-09-06T19:38:22.200280Z", - "shell.execute_reply": "2024-09-06T19:38:22.199770Z" + "iopub.execute_input": "2024-09-26T14:52:23.869442Z", + "iopub.status.busy": "2024-09-26T14:52:23.869086Z", + "iopub.status.idle": "2024-09-26T14:52:23.871992Z", + "shell.execute_reply": "2024-09-26T14:52:23.871490Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.203753Z", - "iopub.status.busy": "2024-09-06T19:38:22.202840Z", - "iopub.status.idle": "2024-09-06T19:38:22.208375Z", - "shell.execute_reply": "2024-09-06T19:38:22.207870Z" + "iopub.execute_input": "2024-09-26T14:52:23.873901Z", + "iopub.status.busy": "2024-09-26T14:52:23.873567Z", + "iopub.status.idle": "2024-09-26T14:52:23.878299Z", + "shell.execute_reply": "2024-09-26T14:52:23.877773Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.211876Z", - "iopub.status.busy": "2024-09-06T19:38:22.210955Z", - "iopub.status.idle": "2024-09-06T19:38:22.243013Z", - "shell.execute_reply": "2024-09-06T19:38:22.242528Z" + "iopub.execute_input": "2024-09-26T14:52:23.880472Z", + "iopub.status.busy": "2024-09-26T14:52:23.880009Z", + "iopub.status.idle": "2024-09-26T14:52:23.917031Z", + "shell.execute_reply": "2024-09-26T14:52:23.916497Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.246118Z", - "iopub.status.busy": "2024-09-06T19:38:22.245468Z", - "iopub.status.idle": "2024-09-06T19:38:22.754137Z", - "shell.execute_reply": "2024-09-06T19:38:22.753573Z" + "iopub.execute_input": "2024-09-26T14:52:23.919143Z", + "iopub.status.busy": "2024-09-26T14:52:23.918754Z", + "iopub.status.idle": "2024-09-26T14:52:24.441145Z", + "shell.execute_reply": "2024-09-26T14:52:24.440578Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.757125Z", - "iopub.status.busy": "2024-09-06T19:38:22.756730Z", - "iopub.status.idle": "2024-09-06T19:38:22.893326Z", - "shell.execute_reply": "2024-09-06T19:38:22.892578Z" + "iopub.execute_input": "2024-09-26T14:52:24.443535Z", + "iopub.status.busy": "2024-09-26T14:52:24.443148Z", + "iopub.status.idle": "2024-09-26T14:52:24.581215Z", + "shell.execute_reply": "2024-09-26T14:52:24.580592Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.896382Z", - "iopub.status.busy": "2024-09-06T19:38:22.896143Z", - "iopub.status.idle": "2024-09-06T19:38:22.903618Z", - "shell.execute_reply": "2024-09-06T19:38:22.903032Z" + "iopub.execute_input": "2024-09-26T14:52:24.583982Z", + "iopub.status.busy": "2024-09-26T14:52:24.583021Z", + "iopub.status.idle": "2024-09-26T14:52:24.591560Z", + "shell.execute_reply": "2024-09-26T14:52:24.591052Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.906322Z", - "iopub.status.busy": "2024-09-06T19:38:22.906102Z", - "iopub.status.idle": "2024-09-06T19:38:22.914842Z", - "shell.execute_reply": "2024-09-06T19:38:22.914319Z" + "iopub.execute_input": "2024-09-26T14:52:24.594472Z", + "iopub.status.busy": "2024-09-26T14:52:24.593722Z", + "iopub.status.idle": "2024-09-26T14:52:24.601463Z", + "shell.execute_reply": "2024-09-26T14:52:24.600918Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.917418Z", - "iopub.status.busy": "2024-09-06T19:38:22.917212Z", - "iopub.status.idle": "2024-09-06T19:38:22.924586Z", - "shell.execute_reply": "2024-09-06T19:38:22.924068Z" + "iopub.execute_input": "2024-09-26T14:52:24.604404Z", + "iopub.status.busy": "2024-09-26T14:52:24.603652Z", + "iopub.status.idle": "2024-09-26T14:52:24.610627Z", + "shell.execute_reply": "2024-09-26T14:52:24.610123Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.927978Z", - "iopub.status.busy": "2024-09-06T19:38:22.927001Z", - "iopub.status.idle": "2024-09-06T19:38:22.932989Z", - "shell.execute_reply": "2024-09-06T19:38:22.932417Z" + "iopub.execute_input": "2024-09-26T14:52:24.613514Z", + "iopub.status.busy": "2024-09-26T14:52:24.612748Z", + "iopub.status.idle": "2024-09-26T14:52:24.618379Z", + "shell.execute_reply": "2024-09-26T14:52:24.617862Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.935455Z", - "iopub.status.busy": "2024-09-06T19:38:22.935286Z", - "iopub.status.idle": "2024-09-06T19:38:22.940366Z", - "shell.execute_reply": "2024-09-06T19:38:22.939926Z" + "iopub.execute_input": "2024-09-26T14:52:24.621206Z", + "iopub.status.busy": "2024-09-26T14:52:24.620459Z", + "iopub.status.idle": "2024-09-26T14:52:24.625372Z", + "shell.execute_reply": "2024-09-26T14:52:24.624794Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.942577Z", - "iopub.status.busy": "2024-09-06T19:38:22.942242Z", - "iopub.status.idle": "2024-09-06T19:38:23.018404Z", - "shell.execute_reply": "2024-09-06T19:38:23.017754Z" + "iopub.execute_input": "2024-09-26T14:52:24.627070Z", + "iopub.status.busy": "2024-09-26T14:52:24.626899Z", + "iopub.status.idle": "2024-09-26T14:52:24.703448Z", + "shell.execute_reply": "2024-09-26T14:52:24.702825Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.021060Z", - "iopub.status.busy": "2024-09-06T19:38:23.020492Z", - "iopub.status.idle": "2024-09-06T19:38:23.034062Z", - "shell.execute_reply": "2024-09-06T19:38:23.033451Z" + "iopub.execute_input": "2024-09-26T14:52:24.705665Z", + "iopub.status.busy": "2024-09-26T14:52:24.705281Z", + "iopub.status.idle": "2024-09-26T14:52:24.718371Z", + "shell.execute_reply": "2024-09-26T14:52:24.717910Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.036553Z", - "iopub.status.busy": "2024-09-06T19:38:23.036240Z", - "iopub.status.idle": "2024-09-06T19:38:23.039008Z", - "shell.execute_reply": "2024-09-06T19:38:23.038465Z" + "iopub.execute_input": "2024-09-26T14:52:24.719953Z", + "iopub.status.busy": "2024-09-26T14:52:24.719774Z", + "iopub.status.idle": "2024-09-26T14:52:24.722525Z", + "shell.execute_reply": "2024-09-26T14:52:24.721993Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.041147Z", - "iopub.status.busy": "2024-09-06T19:38:23.040695Z", - "iopub.status.idle": "2024-09-06T19:38:23.050646Z", - "shell.execute_reply": "2024-09-06T19:38:23.050044Z" + "iopub.execute_input": "2024-09-26T14:52:24.724217Z", + "iopub.status.busy": "2024-09-26T14:52:24.723890Z", + "iopub.status.idle": "2024-09-26T14:52:24.733856Z", + "shell.execute_reply": "2024-09-26T14:52:24.733386Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.053067Z", - "iopub.status.busy": "2024-09-06T19:38:23.052637Z", - "iopub.status.idle": "2024-09-06T19:38:23.059254Z", - "shell.execute_reply": "2024-09-06T19:38:23.058781Z" + "iopub.execute_input": "2024-09-26T14:52:24.735568Z", + "iopub.status.busy": "2024-09-26T14:52:24.735390Z", + "iopub.status.idle": "2024-09-26T14:52:24.741960Z", + "shell.execute_reply": "2024-09-26T14:52:24.741508Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.061114Z", - "iopub.status.busy": "2024-09-06T19:38:23.060934Z", - "iopub.status.idle": "2024-09-06T19:38:23.064369Z", - "shell.execute_reply": "2024-09-06T19:38:23.063906Z" + "iopub.execute_input": "2024-09-26T14:52:24.743631Z", + "iopub.status.busy": "2024-09-26T14:52:24.743288Z", + "iopub.status.idle": "2024-09-26T14:52:24.746500Z", + "shell.execute_reply": "2024-09-26T14:52:24.746046Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.066492Z", - "iopub.status.busy": "2024-09-06T19:38:23.066088Z", - "iopub.status.idle": "2024-09-06T19:38:27.075896Z", - "shell.execute_reply": "2024-09-06T19:38:27.075361Z" + "iopub.execute_input": "2024-09-26T14:52:24.748147Z", + "iopub.status.busy": "2024-09-26T14:52:24.747796Z", + "iopub.status.idle": "2024-09-26T14:52:28.830714Z", + "shell.execute_reply": "2024-09-26T14:52:28.830201Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:27.079119Z", - "iopub.status.busy": "2024-09-06T19:38:27.078209Z", - "iopub.status.idle": "2024-09-06T19:38:27.082469Z", - "shell.execute_reply": "2024-09-06T19:38:27.082025Z" + "iopub.execute_input": "2024-09-26T14:52:28.832745Z", + "iopub.status.busy": "2024-09-26T14:52:28.832361Z", + "iopub.status.idle": "2024-09-26T14:52:28.835718Z", + "shell.execute_reply": "2024-09-26T14:52:28.835165Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:27.084613Z", - "iopub.status.busy": "2024-09-06T19:38:27.084277Z", - "iopub.status.idle": "2024-09-06T19:38:27.087400Z", - "shell.execute_reply": "2024-09-06T19:38:27.086984Z" + "iopub.execute_input": "2024-09-26T14:52:28.837752Z", + "iopub.status.busy": "2024-09-26T14:52:28.837357Z", + "iopub.status.idle": "2024-09-26T14:52:28.840312Z", + "shell.execute_reply": "2024-09-26T14:52:28.839737Z" }, "nbsphinx": "hidden" }, @@ -3492,7 +3492,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index d4d06d3f8..f81022c48 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": "2024-09-06T19:38:29.945055Z", - "iopub.status.busy": "2024-09-06T19:38:29.944859Z", - "iopub.status.idle": "2024-09-06T19:38:31.152677Z", - "shell.execute_reply": "2024-09-06T19:38:31.152154Z" + "iopub.execute_input": "2024-09-26T14:52:32.169125Z", + "iopub.status.busy": "2024-09-26T14:52:32.168956Z", + "iopub.status.idle": "2024-09-26T14:52:33.431499Z", + "shell.execute_reply": "2024-09-26T14:52:33.430884Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:31.155349Z", - "iopub.status.busy": "2024-09-06T19:38:31.154914Z", - "iopub.status.idle": "2024-09-06T19:38:31.333867Z", - "shell.execute_reply": "2024-09-06T19:38:31.333299Z" + "iopub.execute_input": "2024-09-26T14:52:33.434100Z", + "iopub.status.busy": "2024-09-26T14:52:33.433789Z", + "iopub.status.idle": "2024-09-26T14:52:33.621182Z", + "shell.execute_reply": "2024-09-26T14:52:33.620609Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.336296Z", - "iopub.status.busy": "2024-09-06T19:38:31.336106Z", - "iopub.status.idle": "2024-09-06T19:38:31.347492Z", - "shell.execute_reply": "2024-09-06T19:38:31.347045Z" + "iopub.execute_input": "2024-09-26T14:52:33.623560Z", + "iopub.status.busy": "2024-09-26T14:52:33.623109Z", + "iopub.status.idle": "2024-09-26T14:52:33.635370Z", + "shell.execute_reply": "2024-09-26T14:52:33.634790Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.349587Z", - "iopub.status.busy": "2024-09-06T19:38:31.349239Z", - "iopub.status.idle": "2024-09-06T19:38:31.559000Z", - "shell.execute_reply": "2024-09-06T19:38:31.558435Z" + "iopub.execute_input": "2024-09-26T14:52:33.637192Z", + "iopub.status.busy": "2024-09-26T14:52:33.636918Z", + "iopub.status.idle": "2024-09-26T14:52:33.875845Z", + "shell.execute_reply": "2024-09-26T14:52:33.875224Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.561389Z", - "iopub.status.busy": "2024-09-06T19:38:31.561027Z", - "iopub.status.idle": "2024-09-06T19:38:31.587035Z", - "shell.execute_reply": "2024-09-06T19:38:31.586568Z" + "iopub.execute_input": "2024-09-26T14:52:33.877984Z", + "iopub.status.busy": "2024-09-26T14:52:33.877640Z", + "iopub.status.idle": "2024-09-26T14:52:33.905047Z", + "shell.execute_reply": "2024-09-26T14:52:33.904562Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.589259Z", - "iopub.status.busy": "2024-09-06T19:38:31.588898Z", - "iopub.status.idle": "2024-09-06T19:38:33.659672Z", - "shell.execute_reply": "2024-09-06T19:38:33.658986Z" + "iopub.execute_input": "2024-09-26T14:52:33.906945Z", + "iopub.status.busy": "2024-09-26T14:52:33.906618Z", + "iopub.status.idle": "2024-09-26T14:52:36.066124Z", + "shell.execute_reply": "2024-09-26T14:52:36.065509Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:33.662234Z", - "iopub.status.busy": "2024-09-06T19:38:33.661770Z", - "iopub.status.idle": "2024-09-06T19:38:33.679880Z", - "shell.execute_reply": "2024-09-06T19:38:33.679304Z" + "iopub.execute_input": "2024-09-26T14:52:36.068327Z", + "iopub.status.busy": "2024-09-26T14:52:36.067791Z", + "iopub.status.idle": "2024-09-26T14:52:36.085955Z", + "shell.execute_reply": "2024-09-26T14:52:36.085444Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:33.682125Z", - "iopub.status.busy": "2024-09-06T19:38:33.681797Z", - "iopub.status.idle": "2024-09-06T19:38:35.246559Z", - "shell.execute_reply": "2024-09-06T19:38:35.245952Z" + "iopub.execute_input": "2024-09-26T14:52:36.087636Z", + "iopub.status.busy": "2024-09-26T14:52:36.087436Z", + "iopub.status.idle": "2024-09-26T14:52:37.714159Z", + "shell.execute_reply": "2024-09-26T14:52:37.713482Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.249384Z", - "iopub.status.busy": "2024-09-06T19:38:35.248692Z", - "iopub.status.idle": "2024-09-06T19:38:35.262909Z", - "shell.execute_reply": "2024-09-06T19:38:35.262437Z" + "iopub.execute_input": "2024-09-26T14:52:37.716539Z", + "iopub.status.busy": "2024-09-26T14:52:37.715812Z", + "iopub.status.idle": "2024-09-26T14:52:37.730102Z", + "shell.execute_reply": "2024-09-26T14:52:37.729543Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.265091Z", - "iopub.status.busy": "2024-09-06T19:38:35.264657Z", - "iopub.status.idle": "2024-09-06T19:38:35.347361Z", - "shell.execute_reply": "2024-09-06T19:38:35.346752Z" + "iopub.execute_input": "2024-09-26T14:52:37.731957Z", + "iopub.status.busy": "2024-09-26T14:52:37.731617Z", + "iopub.status.idle": "2024-09-26T14:52:37.821262Z", + "shell.execute_reply": "2024-09-26T14:52:37.820618Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.349859Z", - "iopub.status.busy": "2024-09-06T19:38:35.349553Z", - "iopub.status.idle": "2024-09-06T19:38:35.568160Z", - "shell.execute_reply": "2024-09-06T19:38:35.567596Z" + "iopub.execute_input": "2024-09-26T14:52:37.823300Z", + "iopub.status.busy": "2024-09-26T14:52:37.822839Z", + "iopub.status.idle": "2024-09-26T14:52:38.038920Z", + "shell.execute_reply": "2024-09-26T14:52:38.038375Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.570518Z", - "iopub.status.busy": "2024-09-06T19:38:35.570156Z", - "iopub.status.idle": "2024-09-06T19:38:35.587030Z", - "shell.execute_reply": "2024-09-06T19:38:35.586565Z" + "iopub.execute_input": "2024-09-26T14:52:38.040759Z", + "iopub.status.busy": "2024-09-26T14:52:38.040570Z", + "iopub.status.idle": "2024-09-26T14:52:38.058165Z", + "shell.execute_reply": "2024-09-26T14:52:38.057614Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.589095Z", - "iopub.status.busy": "2024-09-06T19:38:35.588739Z", - "iopub.status.idle": "2024-09-06T19:38:35.598220Z", - "shell.execute_reply": "2024-09-06T19:38:35.597755Z" + "iopub.execute_input": "2024-09-26T14:52:38.060074Z", + "iopub.status.busy": "2024-09-26T14:52:38.059687Z", + "iopub.status.idle": "2024-09-26T14:52:38.069888Z", + "shell.execute_reply": "2024-09-26T14:52:38.069309Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.600262Z", - "iopub.status.busy": "2024-09-06T19:38:35.599918Z", - "iopub.status.idle": "2024-09-06T19:38:35.692538Z", - "shell.execute_reply": "2024-09-06T19:38:35.691918Z" + "iopub.execute_input": "2024-09-26T14:52:38.071813Z", + "iopub.status.busy": "2024-09-26T14:52:38.071379Z", + "iopub.status.idle": "2024-09-26T14:52:38.170054Z", + "shell.execute_reply": "2024-09-26T14:52:38.169477Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.694934Z", - "iopub.status.busy": "2024-09-06T19:38:35.694629Z", - "iopub.status.idle": "2024-09-06T19:38:35.833017Z", - "shell.execute_reply": "2024-09-06T19:38:35.832312Z" + "iopub.execute_input": "2024-09-26T14:52:38.171925Z", + "iopub.status.busy": "2024-09-26T14:52:38.171696Z", + "iopub.status.idle": "2024-09-26T14:52:38.324224Z", + "shell.execute_reply": "2024-09-26T14:52:38.323549Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.835595Z", - "iopub.status.busy": "2024-09-06T19:38:35.835206Z", - "iopub.status.idle": "2024-09-06T19:38:35.839051Z", - "shell.execute_reply": "2024-09-06T19:38:35.838497Z" + "iopub.execute_input": "2024-09-26T14:52:38.326329Z", + "iopub.status.busy": "2024-09-26T14:52:38.325951Z", + "iopub.status.idle": "2024-09-26T14:52:38.329903Z", + "shell.execute_reply": "2024-09-26T14:52:38.329357Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.841055Z", - "iopub.status.busy": "2024-09-06T19:38:35.840887Z", - "iopub.status.idle": "2024-09-06T19:38:35.844523Z", - "shell.execute_reply": "2024-09-06T19:38:35.843987Z" + "iopub.execute_input": "2024-09-26T14:52:38.331907Z", + "iopub.status.busy": "2024-09-26T14:52:38.331482Z", + "iopub.status.idle": "2024-09-26T14:52:38.335196Z", + "shell.execute_reply": "2024-09-26T14:52:38.334746Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.846624Z", - "iopub.status.busy": "2024-09-06T19:38:35.846289Z", - "iopub.status.idle": "2024-09-06T19:38:35.883516Z", - "shell.execute_reply": "2024-09-06T19:38:35.883025Z" + "iopub.execute_input": "2024-09-26T14:52:38.336922Z", + "iopub.status.busy": "2024-09-26T14:52:38.336603Z", + "iopub.status.idle": "2024-09-26T14:52:38.376114Z", + "shell.execute_reply": "2024-09-26T14:52:38.375641Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.885707Z", - "iopub.status.busy": "2024-09-06T19:38:35.885360Z", - "iopub.status.idle": "2024-09-06T19:38:35.926415Z", - "shell.execute_reply": "2024-09-06T19:38:35.925951Z" + "iopub.execute_input": "2024-09-26T14:52:38.378083Z", + "iopub.status.busy": "2024-09-26T14:52:38.377733Z", + "iopub.status.idle": "2024-09-26T14:52:38.419996Z", + "shell.execute_reply": "2024-09-26T14:52:38.419527Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.928488Z", - "iopub.status.busy": "2024-09-06T19:38:35.928146Z", - "iopub.status.idle": "2024-09-06T19:38:36.031351Z", - "shell.execute_reply": "2024-09-06T19:38:36.030698Z" + "iopub.execute_input": "2024-09-26T14:52:38.421872Z", + "iopub.status.busy": "2024-09-26T14:52:38.421510Z", + "iopub.status.idle": "2024-09-26T14:52:38.531907Z", + "shell.execute_reply": "2024-09-26T14:52:38.531268Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.034301Z", - "iopub.status.busy": "2024-09-06T19:38:36.033912Z", - "iopub.status.idle": "2024-09-06T19:38:36.132017Z", - "shell.execute_reply": "2024-09-06T19:38:36.131369Z" + "iopub.execute_input": "2024-09-26T14:52:38.534145Z", + "iopub.status.busy": "2024-09-26T14:52:38.533766Z", + "iopub.status.idle": "2024-09-26T14:52:38.651268Z", + "shell.execute_reply": "2024-09-26T14:52:38.650679Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.134718Z", - "iopub.status.busy": "2024-09-06T19:38:36.134254Z", - "iopub.status.idle": "2024-09-06T19:38:36.372737Z", - "shell.execute_reply": "2024-09-06T19:38:36.372155Z" + "iopub.execute_input": "2024-09-26T14:52:38.653171Z", + "iopub.status.busy": "2024-09-26T14:52:38.652916Z", + "iopub.status.idle": "2024-09-26T14:52:38.868009Z", + "shell.execute_reply": "2024-09-26T14:52:38.867481Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.374987Z", - "iopub.status.busy": "2024-09-06T19:38:36.374694Z", - "iopub.status.idle": "2024-09-06T19:38:36.587886Z", - "shell.execute_reply": "2024-09-06T19:38:36.587278Z" + "iopub.execute_input": "2024-09-26T14:52:38.870022Z", + "iopub.status.busy": "2024-09-26T14:52:38.869668Z", + "iopub.status.idle": "2024-09-26T14:52:39.116995Z", + "shell.execute_reply": "2024-09-26T14:52:39.116409Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.590343Z", - "iopub.status.busy": "2024-09-06T19:38:36.589956Z", - "iopub.status.idle": "2024-09-06T19:38:36.595878Z", - "shell.execute_reply": "2024-09-06T19:38:36.595334Z" + "iopub.execute_input": "2024-09-26T14:52:39.119063Z", + "iopub.status.busy": "2024-09-26T14:52:39.118651Z", + "iopub.status.idle": "2024-09-26T14:52:39.124659Z", + "shell.execute_reply": "2024-09-26T14:52:39.124212Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.598057Z", - "iopub.status.busy": "2024-09-06T19:38:36.597740Z", - "iopub.status.idle": "2024-09-06T19:38:36.811700Z", - "shell.execute_reply": "2024-09-06T19:38:36.811079Z" + "iopub.execute_input": "2024-09-26T14:52:39.126372Z", + "iopub.status.busy": "2024-09-26T14:52:39.126025Z", + "iopub.status.idle": "2024-09-26T14:52:39.360620Z", + "shell.execute_reply": "2024-09-26T14:52:39.360015Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.813989Z", - "iopub.status.busy": "2024-09-06T19:38:36.813680Z", - "iopub.status.idle": "2024-09-06T19:38:37.873549Z", - "shell.execute_reply": "2024-09-06T19:38:37.872901Z" + "iopub.execute_input": "2024-09-26T14:52:39.362552Z", + "iopub.status.busy": "2024-09-26T14:52:39.362361Z", + "iopub.status.idle": "2024-09-26T14:52:40.445531Z", + "shell.execute_reply": "2024-09-26T14:52:40.444958Z" }, "id": "wL3ngCnuLEWd" }, @@ -2419,7 +2419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 0b05cce8c..f2aa83ef9 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": "2024-09-06T19:38:41.455901Z", - "iopub.status.busy": "2024-09-06T19:38:41.455732Z", - "iopub.status.idle": "2024-09-06T19:38:42.611358Z", - "shell.execute_reply": "2024-09-06T19:38:42.610733Z" + "iopub.execute_input": "2024-09-26T14:52:44.089068Z", + "iopub.status.busy": "2024-09-26T14:52:44.088906Z", + "iopub.status.idle": "2024-09-26T14:52:45.299550Z", + "shell.execute_reply": "2024-09-26T14:52:45.298928Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:42.614152Z", - "iopub.status.busy": "2024-09-06T19:38:42.613703Z", - "iopub.status.idle": "2024-09-06T19:38:42.617474Z", - "shell.execute_reply": "2024-09-06T19:38:42.616914Z" + "iopub.execute_input": "2024-09-26T14:52:45.301912Z", + "iopub.status.busy": "2024-09-26T14:52:45.301449Z", + "iopub.status.idle": "2024-09-26T14:52:45.304645Z", + "shell.execute_reply": "2024-09-26T14:52:45.304094Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.619686Z", - "iopub.status.busy": "2024-09-06T19:38:42.619396Z", - "iopub.status.idle": "2024-09-06T19:38:42.627253Z", - "shell.execute_reply": "2024-09-06T19:38:42.626804Z" + "iopub.execute_input": "2024-09-26T14:52:45.306413Z", + "iopub.status.busy": "2024-09-26T14:52:45.306142Z", + "iopub.status.idle": "2024-09-26T14:52:45.314151Z", + "shell.execute_reply": "2024-09-26T14:52:45.313702Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.629251Z", - "iopub.status.busy": "2024-09-06T19:38:42.628912Z", - "iopub.status.idle": "2024-09-06T19:38:42.675739Z", - "shell.execute_reply": "2024-09-06T19:38:42.675250Z" + "iopub.execute_input": "2024-09-26T14:52:45.315923Z", + "iopub.status.busy": "2024-09-26T14:52:45.315583Z", + "iopub.status.idle": "2024-09-26T14:52:45.364795Z", + "shell.execute_reply": "2024-09-26T14:52:45.364189Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.677746Z", - "iopub.status.busy": "2024-09-06T19:38:42.677566Z", - "iopub.status.idle": "2024-09-06T19:38:42.695187Z", - "shell.execute_reply": "2024-09-06T19:38:42.694600Z" + "iopub.execute_input": "2024-09-26T14:52:45.371300Z", + "iopub.status.busy": "2024-09-26T14:52:45.370858Z", + "iopub.status.idle": "2024-09-26T14:52:45.389579Z", + "shell.execute_reply": "2024-09-26T14:52:45.389064Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.697240Z", - "iopub.status.busy": "2024-09-06T19:38:42.696927Z", - "iopub.status.idle": "2024-09-06T19:38:42.700805Z", - "shell.execute_reply": "2024-09-06T19:38:42.700357Z" + "iopub.execute_input": "2024-09-26T14:52:45.391559Z", + "iopub.status.busy": "2024-09-26T14:52:45.391112Z", + "iopub.status.idle": "2024-09-26T14:52:45.395156Z", + "shell.execute_reply": "2024-09-26T14:52:45.394627Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.703011Z", - "iopub.status.busy": "2024-09-06T19:38:42.702619Z", - "iopub.status.idle": "2024-09-06T19:38:42.719152Z", - "shell.execute_reply": "2024-09-06T19:38:42.718696Z" + "iopub.execute_input": "2024-09-26T14:52:45.397035Z", + "iopub.status.busy": "2024-09-26T14:52:45.396725Z", + "iopub.status.idle": "2024-09-26T14:52:45.414290Z", + "shell.execute_reply": "2024-09-26T14:52:45.413687Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.721153Z", - "iopub.status.busy": "2024-09-06T19:38:42.720797Z", - "iopub.status.idle": "2024-09-06T19:38:42.746197Z", - "shell.execute_reply": "2024-09-06T19:38:42.745739Z" + "iopub.execute_input": "2024-09-26T14:52:45.416157Z", + "iopub.status.busy": "2024-09-26T14:52:45.415806Z", + "iopub.status.idle": "2024-09-26T14:52:45.442358Z", + "shell.execute_reply": "2024-09-26T14:52:45.441883Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.748111Z", - "iopub.status.busy": "2024-09-06T19:38:42.747776Z", - "iopub.status.idle": "2024-09-06T19:38:44.708904Z", - "shell.execute_reply": "2024-09-06T19:38:44.708307Z" + "iopub.execute_input": "2024-09-26T14:52:45.444293Z", + "iopub.status.busy": "2024-09-26T14:52:45.443936Z", + "iopub.status.idle": "2024-09-26T14:52:47.450691Z", + "shell.execute_reply": "2024-09-26T14:52:47.450163Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.711480Z", - "iopub.status.busy": "2024-09-06T19:38:44.710993Z", - "iopub.status.idle": "2024-09-06T19:38:44.717750Z", - "shell.execute_reply": "2024-09-06T19:38:44.717182Z" + "iopub.execute_input": "2024-09-26T14:52:47.452884Z", + "iopub.status.busy": "2024-09-26T14:52:47.452391Z", + "iopub.status.idle": "2024-09-26T14:52:47.459433Z", + "shell.execute_reply": "2024-09-26T14:52:47.458958Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.719963Z", - "iopub.status.busy": "2024-09-06T19:38:44.719631Z", - "iopub.status.idle": "2024-09-06T19:38:44.732695Z", - "shell.execute_reply": "2024-09-06T19:38:44.732259Z" + "iopub.execute_input": "2024-09-26T14:52:47.461250Z", + "iopub.status.busy": "2024-09-26T14:52:47.460913Z", + "iopub.status.idle": "2024-09-26T14:52:47.473767Z", + "shell.execute_reply": "2024-09-26T14:52:47.473271Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.734719Z", - "iopub.status.busy": "2024-09-06T19:38:44.734386Z", - "iopub.status.idle": "2024-09-06T19:38:44.740630Z", - "shell.execute_reply": "2024-09-06T19:38:44.740080Z" + "iopub.execute_input": "2024-09-26T14:52:47.475532Z", + "iopub.status.busy": "2024-09-26T14:52:47.475187Z", + "iopub.status.idle": "2024-09-26T14:52:47.481746Z", + "shell.execute_reply": "2024-09-26T14:52:47.481272Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.742715Z", - "iopub.status.busy": "2024-09-06T19:38:44.742407Z", - "iopub.status.idle": "2024-09-06T19:38:44.745203Z", - "shell.execute_reply": "2024-09-06T19:38:44.744635Z" + "iopub.execute_input": "2024-09-26T14:52:47.483691Z", + "iopub.status.busy": "2024-09-26T14:52:47.483212Z", + "iopub.status.idle": "2024-09-26T14:52:47.486088Z", + "shell.execute_reply": "2024-09-26T14:52:47.485626Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.747300Z", - "iopub.status.busy": "2024-09-06T19:38:44.746906Z", - "iopub.status.idle": "2024-09-06T19:38:44.750594Z", - "shell.execute_reply": "2024-09-06T19:38:44.750021Z" + "iopub.execute_input": "2024-09-26T14:52:47.487800Z", + "iopub.status.busy": "2024-09-26T14:52:47.487397Z", + "iopub.status.idle": "2024-09-26T14:52:47.491109Z", + "shell.execute_reply": "2024-09-26T14:52:47.490533Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.752864Z", - "iopub.status.busy": "2024-09-06T19:38:44.752447Z", - "iopub.status.idle": "2024-09-06T19:38:44.755290Z", - "shell.execute_reply": "2024-09-06T19:38:44.754743Z" + "iopub.execute_input": "2024-09-26T14:52:47.493003Z", + "iopub.status.busy": "2024-09-26T14:52:47.492607Z", + "iopub.status.idle": "2024-09-26T14:52:47.495261Z", + "shell.execute_reply": "2024-09-26T14:52:47.494806Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.757347Z", - "iopub.status.busy": "2024-09-06T19:38:44.757015Z", - "iopub.status.idle": "2024-09-06T19:38:44.761164Z", - "shell.execute_reply": "2024-09-06T19:38:44.760669Z" + "iopub.execute_input": "2024-09-26T14:52:47.497043Z", + "iopub.status.busy": "2024-09-26T14:52:47.496706Z", + "iopub.status.idle": "2024-09-26T14:52:47.500642Z", + "shell.execute_reply": "2024-09-26T14:52:47.500187Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.763225Z", - "iopub.status.busy": "2024-09-06T19:38:44.762830Z", - "iopub.status.idle": "2024-09-06T19:38:44.791503Z", - "shell.execute_reply": "2024-09-06T19:38:44.790922Z" + "iopub.execute_input": "2024-09-26T14:52:47.502313Z", + "iopub.status.busy": "2024-09-26T14:52:47.502139Z", + "iopub.status.idle": "2024-09-26T14:52:47.531332Z", + "shell.execute_reply": "2024-09-26T14:52:47.530848Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.793778Z", - "iopub.status.busy": "2024-09-06T19:38:44.793374Z", - "iopub.status.idle": "2024-09-06T19:38:44.798051Z", - "shell.execute_reply": "2024-09-06T19:38:44.797497Z" + "iopub.execute_input": "2024-09-26T14:52:47.533361Z", + "iopub.status.busy": "2024-09-26T14:52:47.532995Z", + "iopub.status.idle": "2024-09-26T14:52:47.537680Z", + "shell.execute_reply": "2024-09-26T14:52:47.537223Z" }, "nbsphinx": "hidden" }, @@ -1571,7 +1571,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 7626ff8d8..9b60292d7 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": "2024-09-06T19:38:47.803342Z", - "iopub.status.busy": "2024-09-06T19:38:47.803172Z", - "iopub.status.idle": "2024-09-06T19:38:49.010459Z", - "shell.execute_reply": "2024-09-06T19:38:49.009894Z" + "iopub.execute_input": "2024-09-26T14:52:50.516908Z", + "iopub.status.busy": "2024-09-26T14:52:50.516724Z", + "iopub.status.idle": "2024-09-26T14:52:51.779618Z", + "shell.execute_reply": "2024-09-26T14:52:51.779002Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:49.013219Z", - "iopub.status.busy": "2024-09-06T19:38:49.012725Z", - "iopub.status.idle": "2024-09-06T19:38:49.210289Z", - "shell.execute_reply": "2024-09-06T19:38:49.209783Z" + "iopub.execute_input": "2024-09-26T14:52:51.781880Z", + "iopub.status.busy": "2024-09-26T14:52:51.781585Z", + "iopub.status.idle": "2024-09-26T14:52:51.979199Z", + "shell.execute_reply": "2024-09-26T14:52:51.978560Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:49.212873Z", - "iopub.status.busy": "2024-09-06T19:38:49.212501Z", - "iopub.status.idle": "2024-09-06T19:38:49.226305Z", - "shell.execute_reply": "2024-09-06T19:38:49.225843Z" + "iopub.execute_input": "2024-09-26T14:52:51.981718Z", + "iopub.status.busy": "2024-09-26T14:52:51.981227Z", + "iopub.status.idle": "2024-09-26T14:52:51.994745Z", + "shell.execute_reply": "2024-09-26T14:52:51.994150Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:49.228339Z", - "iopub.status.busy": "2024-09-06T19:38:49.227999Z", - "iopub.status.idle": "2024-09-06T19:38:51.870134Z", - "shell.execute_reply": "2024-09-06T19:38:51.869617Z" + "iopub.execute_input": "2024-09-26T14:52:51.996498Z", + "iopub.status.busy": "2024-09-26T14:52:51.996168Z", + "iopub.status.idle": "2024-09-26T14:52:54.626693Z", + "shell.execute_reply": "2024-09-26T14:52:54.626198Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:51.872305Z", - "iopub.status.busy": "2024-09-06T19:38:51.872107Z", - "iopub.status.idle": "2024-09-06T19:38:53.221496Z", - "shell.execute_reply": "2024-09-06T19:38:53.220930Z" + "iopub.execute_input": "2024-09-26T14:52:54.628558Z", + "iopub.status.busy": "2024-09-26T14:52:54.628209Z", + "iopub.status.idle": "2024-09-26T14:52:55.959728Z", + "shell.execute_reply": "2024-09-26T14:52:55.959162Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:53.223970Z", - "iopub.status.busy": "2024-09-06T19:38:53.223773Z", - "iopub.status.idle": "2024-09-06T19:38:53.227537Z", - "shell.execute_reply": "2024-09-06T19:38:53.226991Z" + "iopub.execute_input": "2024-09-26T14:52:55.962013Z", + "iopub.status.busy": "2024-09-26T14:52:55.961551Z", + "iopub.status.idle": "2024-09-26T14:52:55.965395Z", + "shell.execute_reply": "2024-09-26T14:52:55.964876Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:53.229541Z", - "iopub.status.busy": "2024-09-06T19:38:53.229360Z", - "iopub.status.idle": "2024-09-06T19:38:55.301308Z", - "shell.execute_reply": "2024-09-06T19:38:55.300645Z" + "iopub.execute_input": "2024-09-26T14:52:55.967241Z", + "iopub.status.busy": "2024-09-26T14:52:55.966882Z", + "iopub.status.idle": "2024-09-26T14:52:58.123639Z", + "shell.execute_reply": "2024-09-26T14:52:58.123040Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:55.303915Z", - "iopub.status.busy": "2024-09-06T19:38:55.303372Z", - "iopub.status.idle": "2024-09-06T19:38:55.311571Z", - "shell.execute_reply": "2024-09-06T19:38:55.311093Z" + "iopub.execute_input": "2024-09-26T14:52:58.126062Z", + "iopub.status.busy": "2024-09-26T14:52:58.125463Z", + "iopub.status.idle": "2024-09-26T14:52:58.134883Z", + "shell.execute_reply": "2024-09-26T14:52:58.134421Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:55.313528Z", - "iopub.status.busy": "2024-09-06T19:38:55.313186Z", - "iopub.status.idle": "2024-09-06T19:38:58.079187Z", - "shell.execute_reply": "2024-09-06T19:38:58.078607Z" + "iopub.execute_input": "2024-09-26T14:52:58.136727Z", + "iopub.status.busy": "2024-09-26T14:52:58.136398Z", + "iopub.status.idle": "2024-09-26T14:53:00.725562Z", + "shell.execute_reply": "2024-09-26T14:53:00.724908Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.081586Z", - "iopub.status.busy": "2024-09-06T19:38:58.081221Z", - "iopub.status.idle": "2024-09-06T19:38:58.084505Z", - "shell.execute_reply": "2024-09-06T19:38:58.083969Z" + "iopub.execute_input": "2024-09-26T14:53:00.727650Z", + "iopub.status.busy": "2024-09-26T14:53:00.727262Z", + "iopub.status.idle": "2024-09-26T14:53:00.731306Z", + "shell.execute_reply": "2024-09-26T14:53:00.730747Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.086650Z", - "iopub.status.busy": "2024-09-06T19:38:58.086312Z", - "iopub.status.idle": "2024-09-06T19:38:58.089596Z", - "shell.execute_reply": "2024-09-06T19:38:58.089116Z" + "iopub.execute_input": "2024-09-26T14:53:00.733136Z", + "iopub.status.busy": "2024-09-26T14:53:00.732824Z", + "iopub.status.idle": "2024-09-26T14:53:00.736387Z", + "shell.execute_reply": "2024-09-26T14:53:00.735914Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.091573Z", - "iopub.status.busy": "2024-09-06T19:38:58.091252Z", - "iopub.status.idle": "2024-09-06T19:38:58.095249Z", - "shell.execute_reply": "2024-09-06T19:38:58.094671Z" + "iopub.execute_input": "2024-09-26T14:53:00.738211Z", + "iopub.status.busy": "2024-09-26T14:53:00.737791Z", + "iopub.status.idle": "2024-09-26T14:53:00.740949Z", + "shell.execute_reply": "2024-09-26T14:53:00.740494Z" }, "nbsphinx": "hidden" }, @@ -804,7 +804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index d7703f8af..1a465fa59 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": "2024-09-06T19:39:00.696602Z", - "iopub.status.busy": "2024-09-06T19:39:00.696186Z", - "iopub.status.idle": "2024-09-06T19:39:01.907009Z", - "shell.execute_reply": "2024-09-06T19:39:01.906453Z" + "iopub.execute_input": "2024-09-26T14:53:03.303111Z", + "iopub.status.busy": "2024-09-26T14:53:03.302931Z", + "iopub.status.idle": "2024-09-26T14:53:04.571865Z", + "shell.execute_reply": "2024-09-26T14:53:04.571288Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:01.909568Z", - "iopub.status.busy": "2024-09-06T19:39:01.909050Z", - "iopub.status.idle": "2024-09-06T19:39:04.631163Z", - "shell.execute_reply": "2024-09-06T19:39:04.630426Z" + "iopub.execute_input": "2024-09-26T14:53:04.574087Z", + "iopub.status.busy": "2024-09-26T14:53:04.573598Z", + "iopub.status.idle": "2024-09-26T14:53:06.166960Z", + "shell.execute_reply": "2024-09-26T14:53:06.166164Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.633881Z", - "iopub.status.busy": "2024-09-06T19:39:04.633499Z", - "iopub.status.idle": "2024-09-06T19:39:04.637616Z", - "shell.execute_reply": "2024-09-06T19:39:04.637024Z" + "iopub.execute_input": "2024-09-26T14:53:06.169408Z", + "iopub.status.busy": "2024-09-26T14:53:06.168985Z", + "iopub.status.idle": "2024-09-26T14:53:06.172322Z", + "shell.execute_reply": "2024-09-26T14:53:06.171868Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.639736Z", - "iopub.status.busy": "2024-09-06T19:39:04.639557Z", - "iopub.status.idle": "2024-09-06T19:39:04.646473Z", - "shell.execute_reply": "2024-09-06T19:39:04.646014Z" + "iopub.execute_input": "2024-09-26T14:53:06.174071Z", + "iopub.status.busy": "2024-09-26T14:53:06.173721Z", + "iopub.status.idle": "2024-09-26T14:53:06.180705Z", + "shell.execute_reply": "2024-09-26T14:53:06.180264Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.648396Z", - "iopub.status.busy": "2024-09-06T19:39:04.648219Z", - "iopub.status.idle": "2024-09-06T19:39:05.143459Z", - "shell.execute_reply": "2024-09-06T19:39:05.142840Z" + "iopub.execute_input": "2024-09-26T14:53:06.182537Z", + "iopub.status.busy": "2024-09-26T14:53:06.182190Z", + "iopub.status.idle": "2024-09-26T14:53:06.687592Z", + "shell.execute_reply": "2024-09-26T14:53:06.686965Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.146327Z", - "iopub.status.busy": "2024-09-06T19:39:05.146000Z", - "iopub.status.idle": "2024-09-06T19:39:05.151442Z", - "shell.execute_reply": "2024-09-06T19:39:05.150979Z" + "iopub.execute_input": "2024-09-26T14:53:06.689555Z", + "iopub.status.busy": "2024-09-26T14:53:06.689377Z", + "iopub.status.idle": "2024-09-26T14:53:06.695403Z", + "shell.execute_reply": "2024-09-26T14:53:06.694799Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.153485Z", - "iopub.status.busy": "2024-09-06T19:39:05.153173Z", - "iopub.status.idle": "2024-09-06T19:39:05.157137Z", - "shell.execute_reply": "2024-09-06T19:39:05.156658Z" + "iopub.execute_input": "2024-09-26T14:53:06.697090Z", + "iopub.status.busy": "2024-09-26T14:53:06.696909Z", + "iopub.status.idle": "2024-09-26T14:53:06.700584Z", + "shell.execute_reply": "2024-09-26T14:53:06.700149Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.159200Z", - "iopub.status.busy": "2024-09-06T19:39:05.158859Z", - "iopub.status.idle": "2024-09-06T19:39:06.019168Z", - "shell.execute_reply": "2024-09-06T19:39:06.018545Z" + "iopub.execute_input": "2024-09-26T14:53:06.702385Z", + "iopub.status.busy": "2024-09-26T14:53:06.702049Z", + "iopub.status.idle": "2024-09-26T14:53:07.596906Z", + "shell.execute_reply": "2024-09-26T14:53:07.596232Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.021668Z", - "iopub.status.busy": "2024-09-06T19:39:06.021221Z", - "iopub.status.idle": "2024-09-06T19:39:06.237090Z", - "shell.execute_reply": "2024-09-06T19:39:06.236553Z" + "iopub.execute_input": "2024-09-26T14:53:07.599111Z", + "iopub.status.busy": "2024-09-26T14:53:07.598647Z", + "iopub.status.idle": "2024-09-26T14:53:07.803313Z", + "shell.execute_reply": "2024-09-26T14:53:07.802716Z" } }, "outputs": [ @@ -627,7 +627,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered.\n" + "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -660,10 +667,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.239343Z", - "iopub.status.busy": "2024-09-06T19:39:06.238930Z", - "iopub.status.idle": "2024-09-06T19:39:06.243194Z", - "shell.execute_reply": "2024-09-06T19:39:06.242735Z" + "iopub.execute_input": "2024-09-26T14:53:07.805415Z", + "iopub.status.busy": "2024-09-26T14:53:07.804927Z", + "iopub.status.idle": "2024-09-26T14:53:07.809280Z", + "shell.execute_reply": "2024-09-26T14:53:07.808847Z" } }, "outputs": [ @@ -700,10 +707,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.245282Z", - "iopub.status.busy": "2024-09-06T19:39:06.244951Z", - "iopub.status.idle": "2024-09-06T19:39:06.697627Z", - "shell.execute_reply": "2024-09-06T19:39:06.697015Z" + "iopub.execute_input": "2024-09-26T14:53:07.810942Z", + "iopub.status.busy": "2024-09-26T14:53:07.810764Z", + "iopub.status.idle": "2024-09-26T14:53:08.277163Z", + "shell.execute_reply": "2024-09-26T14:53:08.276574Z" } }, "outputs": [ @@ -762,10 +769,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.700924Z", - "iopub.status.busy": "2024-09-06T19:39:06.700539Z", - "iopub.status.idle": "2024-09-06T19:39:07.035472Z", - "shell.execute_reply": "2024-09-06T19:39:07.034925Z" + "iopub.execute_input": "2024-09-26T14:53:08.279934Z", + "iopub.status.busy": "2024-09-26T14:53:08.279727Z", + "iopub.status.idle": "2024-09-26T14:53:08.615867Z", + "shell.execute_reply": "2024-09-26T14:53:08.615304Z" } }, "outputs": [ @@ -812,10 +819,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.038382Z", - "iopub.status.busy": "2024-09-06T19:39:07.038001Z", - "iopub.status.idle": "2024-09-06T19:39:07.401507Z", - "shell.execute_reply": "2024-09-06T19:39:07.400918Z" + "iopub.execute_input": "2024-09-26T14:53:08.617985Z", + "iopub.status.busy": "2024-09-26T14:53:08.617788Z", + "iopub.status.idle": "2024-09-26T14:53:08.987995Z", + "shell.execute_reply": "2024-09-26T14:53:08.987382Z" } }, "outputs": [ @@ -862,10 +869,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.404511Z", - "iopub.status.busy": "2024-09-06T19:39:07.404090Z", - "iopub.status.idle": "2024-09-06T19:39:07.846501Z", - "shell.execute_reply": "2024-09-06T19:39:07.845952Z" + "iopub.execute_input": "2024-09-26T14:53:08.990870Z", + "iopub.status.busy": "2024-09-26T14:53:08.990636Z", + "iopub.status.idle": "2024-09-26T14:53:09.438626Z", + "shell.execute_reply": "2024-09-26T14:53:09.438065Z" } }, "outputs": [ @@ -925,10 +932,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.851154Z", - "iopub.status.busy": "2024-09-06T19:39:07.850706Z", - "iopub.status.idle": "2024-09-06T19:39:08.296657Z", - "shell.execute_reply": "2024-09-06T19:39:08.296063Z" + "iopub.execute_input": "2024-09-26T14:53:09.442663Z", + "iopub.status.busy": "2024-09-26T14:53:09.442289Z", + "iopub.status.idle": "2024-09-26T14:53:09.875533Z", + "shell.execute_reply": "2024-09-26T14:53:09.874886Z" } }, "outputs": [ @@ -971,10 +978,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.300087Z", - "iopub.status.busy": "2024-09-06T19:39:08.299623Z", - "iopub.status.idle": "2024-09-06T19:39:08.513354Z", - "shell.execute_reply": "2024-09-06T19:39:08.512755Z" + "iopub.execute_input": "2024-09-26T14:53:09.878235Z", + "iopub.status.busy": "2024-09-26T14:53:09.877876Z", + "iopub.status.idle": "2024-09-26T14:53:10.074349Z", + "shell.execute_reply": "2024-09-26T14:53:10.073721Z" } }, "outputs": [ @@ -1017,10 +1024,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.515572Z", - "iopub.status.busy": "2024-09-06T19:39:08.515168Z", - "iopub.status.idle": "2024-09-06T19:39:08.694654Z", - "shell.execute_reply": "2024-09-06T19:39:08.694085Z" + "iopub.execute_input": "2024-09-26T14:53:10.076454Z", + "iopub.status.busy": "2024-09-26T14:53:10.076093Z", + "iopub.status.idle": "2024-09-26T14:53:10.258000Z", + "shell.execute_reply": "2024-09-26T14:53:10.257430Z" } }, "outputs": [ @@ -1067,10 +1074,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.697419Z", - "iopub.status.busy": "2024-09-06T19:39:08.697030Z", - "iopub.status.idle": "2024-09-06T19:39:08.699909Z", - "shell.execute_reply": "2024-09-06T19:39:08.699453Z" + "iopub.execute_input": "2024-09-26T14:53:10.260221Z", + "iopub.status.busy": "2024-09-26T14:53:10.259868Z", + "iopub.status.idle": "2024-09-26T14:53:10.262670Z", + "shell.execute_reply": "2024-09-26T14:53:10.262238Z" } }, "outputs": [], @@ -1090,10 +1097,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.701948Z", - "iopub.status.busy": "2024-09-06T19:39:08.701622Z", - "iopub.status.idle": "2024-09-06T19:39:09.635839Z", - "shell.execute_reply": "2024-09-06T19:39:09.635227Z" + "iopub.execute_input": "2024-09-26T14:53:10.264357Z", + "iopub.status.busy": "2024-09-26T14:53:10.264032Z", + "iopub.status.idle": "2024-09-26T14:53:11.303194Z", + "shell.execute_reply": "2024-09-26T14:53:11.302561Z" } }, "outputs": [ @@ -1172,10 +1179,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.637949Z", - "iopub.status.busy": "2024-09-06T19:39:09.637773Z", - "iopub.status.idle": "2024-09-06T19:39:09.767317Z", - "shell.execute_reply": "2024-09-06T19:39:09.766833Z" + "iopub.execute_input": "2024-09-26T14:53:11.305028Z", + "iopub.status.busy": "2024-09-26T14:53:11.304725Z", + "iopub.status.idle": "2024-09-26T14:53:11.509799Z", + "shell.execute_reply": "2024-09-26T14:53:11.509285Z" } }, "outputs": [ @@ -1214,10 +1221,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.769238Z", - "iopub.status.busy": "2024-09-06T19:39:09.769067Z", - "iopub.status.idle": "2024-09-06T19:39:09.969227Z", - "shell.execute_reply": "2024-09-06T19:39:09.968617Z" + "iopub.execute_input": "2024-09-26T14:53:11.511395Z", + "iopub.status.busy": "2024-09-26T14:53:11.511212Z", + "iopub.status.idle": "2024-09-26T14:53:11.718820Z", + "shell.execute_reply": "2024-09-26T14:53:11.718199Z" } }, "outputs": [], @@ -1266,10 +1273,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.971377Z", - "iopub.status.busy": "2024-09-06T19:39:09.971032Z", - "iopub.status.idle": "2024-09-06T19:39:10.691109Z", - "shell.execute_reply": "2024-09-06T19:39:10.690570Z" + "iopub.execute_input": "2024-09-26T14:53:11.720947Z", + "iopub.status.busy": "2024-09-26T14:53:11.720765Z", + "iopub.status.idle": "2024-09-26T14:53:12.421538Z", + "shell.execute_reply": "2024-09-26T14:53:12.420820Z" } }, "outputs": [ @@ -1351,10 +1358,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:10.693528Z", - "iopub.status.busy": "2024-09-06T19:39:10.693149Z", - "iopub.status.idle": "2024-09-06T19:39:10.697005Z", - "shell.execute_reply": "2024-09-06T19:39:10.696512Z" + "iopub.execute_input": "2024-09-26T14:53:12.423286Z", + "iopub.status.busy": "2024-09-26T14:53:12.423091Z", + "iopub.status.idle": "2024-09-26T14:53:12.427074Z", + "shell.execute_reply": "2024-09-26T14:53:12.426599Z" }, "nbsphinx": "hidden" }, @@ -1387,7 +1394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index ab02f6a16..82b2532b4 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": "2024-09-06T19:39:13.100046Z", - "iopub.status.busy": "2024-09-06T19:39:13.099622Z", - "iopub.status.idle": "2024-09-06T19:39:15.925691Z", - "shell.execute_reply": "2024-09-06T19:39:15.925058Z" + "iopub.execute_input": "2024-09-26T14:53:14.827019Z", + "iopub.status.busy": "2024-09-26T14:53:14.826845Z", + "iopub.status.idle": "2024-09-26T14:53:17.796587Z", + "shell.execute_reply": "2024-09-26T14:53:17.795936Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:15.928762Z", - "iopub.status.busy": "2024-09-06T19:39:15.928196Z", - "iopub.status.idle": "2024-09-06T19:39:16.252610Z", - "shell.execute_reply": "2024-09-06T19:39:16.252054Z" + "iopub.execute_input": "2024-09-26T14:53:17.798905Z", + "iopub.status.busy": "2024-09-26T14:53:17.798584Z", + "iopub.status.idle": "2024-09-26T14:53:18.137749Z", + "shell.execute_reply": "2024-09-26T14:53:18.137173Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:16.255233Z", - "iopub.status.busy": "2024-09-06T19:39:16.254751Z", - "iopub.status.idle": "2024-09-06T19:39:16.259089Z", - "shell.execute_reply": "2024-09-06T19:39:16.258660Z" + "iopub.execute_input": "2024-09-26T14:53:18.139715Z", + "iopub.status.busy": "2024-09-26T14:53:18.139407Z", + "iopub.status.idle": "2024-09-26T14:53:18.143870Z", + "shell.execute_reply": "2024-09-26T14:53:18.143450Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:16.261376Z", - "iopub.status.busy": "2024-09-06T19:39:16.260945Z", - "iopub.status.idle": "2024-09-06T19:39:23.300858Z", - "shell.execute_reply": "2024-09-06T19:39:23.300244Z" + "iopub.execute_input": "2024-09-26T14:53:18.145657Z", + "iopub.status.busy": "2024-09-26T14:53:18.145384Z", + "iopub.status.idle": "2024-09-26T14:53:24.392739Z", + "shell.execute_reply": "2024-09-26T14:53:24.392209Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<09:50, 288460.96it/s]" + " 1%| | 1212416/170498071 [00:00<00:14, 12024376.95it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<02:31, 1124759.70it/s]" + " 4%|▎ | 6160384/170498071 [00:00<00:04, 33857865.84it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:52, 3225591.40it/s]" + " 6%|▌ | 10518528/170498071 [00:00<00:04, 38150209.28it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 3571712/170498071 [00:00<00:14, 11574707.14it/s]" + " 9%|▉ | 15400960/170498071 [00:00<00:03, 42330857.28it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9633792/170498071 [00:00<00:06, 25807611.79it/s]" + " 12%|█▏ | 20250624/170498071 [00:00<00:03, 44424970.42it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15892480/170498071 [00:00<00:04, 35393042.76it/s]" + " 15%|█▍ | 24739840/170498071 [00:00<00:03, 44347437.97it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22052864/170498071 [00:00<00:03, 41375940.12it/s]" + " 17%|█▋ | 29294592/170498071 [00:00<00:03, 44719226.63it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 27918336/170498071 [00:00<00:03, 46336247.02it/s]" + " 20%|██ | 34144256/170498071 [00:00<00:02, 45791541.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32604160/170498071 [00:01<00:03, 45410241.06it/s]" + " 23%|██▎ | 38731776/170498071 [00:00<00:02, 45062771.26it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37978112/170498071 [00:01<00:02, 46512554.13it/s]" + " 25%|██▌ | 43253760/170498071 [00:01<00:02, 45089662.43it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 44072960/170498071 [00:01<00:02, 50196826.35it/s]" + " 28%|██▊ | 47874048/170498071 [00:01<00:02, 45213443.22it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49217536/170498071 [00:01<00:02, 50515326.91it/s]" + " 31%|███ | 52494336/170498071 [00:01<00:02, 45379651.12it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54296576/170498071 [00:01<00:02, 49331301.44it/s]" + " 33%|███▎ | 57049088/170498071 [00:01<00:02, 44930180.76it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 60129280/170498071 [00:01<00:02, 51745509.08it/s]" + " 36%|███▌ | 61571072/170498071 [00:01<00:02, 43892355.61it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 65339392/170498071 [00:01<00:02, 51498978.62it/s]" + " 39%|███▊ | 65994752/170498071 [00:01<00:02, 43763301.63it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70516736/170498071 [00:01<00:01, 50172708.54it/s]" + " 41%|████▏ | 70385664/170498071 [00:01<00:02, 43438744.69it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76251136/170498071 [00:01<00:01, 52173671.62it/s]" + " 44%|████▍ | 75104256/170498071 [00:01<00:02, 44425115.39it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 81559552/170498071 [00:01<00:01, 52429909.15it/s]" + " 47%|████▋ | 79855616/170498071 [00:01<00:02, 45166993.85it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 86835200/170498071 [00:02<00:01, 50316420.17it/s]" + " 49%|████▉ | 84377600/170498071 [00:01<00:01, 43789983.48it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 92438528/170498071 [00:02<00:01, 51729464.30it/s]" + " 52%|█████▏ | 88768512/170498071 [00:02<00:01, 43106787.26it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 97878016/170498071 [00:02<00:01, 52469802.74it/s]" + " 55%|█████▍ | 93093888/170498071 [00:02<00:01, 42763173.18it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103153664/170498071 [00:02<00:01, 51263628.20it/s]" + " 57%|█████▋ | 97386496/170498071 [00:02<00:01, 42678693.69it/s]" ] }, { @@ -428,7 +428,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 108396544/170498071 [00:02<00:01, 51439851.19it/s]" + " 60%|█████▉ | 101679104/170498071 [00:02<00:01, 42558052.16it/s]" ] }, { @@ -436,7 +436,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 114130944/170498071 [00:02<00:01, 53113973.23it/s]" + " 62%|██████▏ | 106102784/170498071 [00:02<00:01, 43049601.15it/s]" ] }, { @@ -444,7 +444,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 119472128/170498071 [00:02<00:00, 51879482.02it/s]" + " 65%|██████▍ | 110592000/170498071 [00:02<00:01, 43553293.68it/s]" ] }, { @@ -452,7 +452,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 124682240/170498071 [00:02<00:00, 50047274.18it/s]" + " 67%|██████▋ | 114950144/170498071 [00:02<00:01, 43398814.53it/s]" ] }, { @@ -460,7 +460,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130547712/170498071 [00:02<00:00, 52494107.90it/s]" + " 70%|██████▉ | 119308288/170498071 [00:02<00:01, 43218682.93it/s]" ] }, { @@ -468,7 +468,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 135823360/170498071 [00:03<00:00, 52004524.51it/s]" + " 73%|███████▎ | 125075456/170498071 [00:02<00:00, 47414945.57it/s]" ] }, { @@ -476,7 +476,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141066240/170498071 [00:03<00:00, 50983301.18it/s]" + " 78%|███████▊ | 133234688/170498071 [00:02<00:00, 57528916.08it/s]" ] }, { @@ -484,7 +484,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 146636800/170498071 [00:03<00:00, 52034590.57it/s]" + " 83%|████████▎ | 141262848/170498071 [00:03<00:00, 64272093.96it/s]" ] }, { @@ -492,7 +492,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 151879680/170498071 [00:03<00:00, 52140968.39it/s]" + " 87%|████████▋ | 149127168/170498071 [00:03<00:00, 68499385.74it/s]" ] }, { @@ -500,7 +500,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 157122560/170498071 [00:03<00:00, 50962142.96it/s]" + " 92%|█████████▏| 157024256/170498071 [00:03<00:00, 71592148.49it/s]" ] }, { @@ -508,7 +508,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162463744/170498071 [00:03<00:00, 51228143.58it/s]" + " 97%|█████████▋| 165117952/170498071 [00:03<00:00, 74385700.68it/s]" ] }, { @@ -516,15 +516,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▊| 168329216/170498071 [00:03<00:00, 53366850.10it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 46456493.64it/s]" + "100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]" ] }, { @@ -642,10 +634,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.303328Z", - "iopub.status.busy": "2024-09-06T19:39:23.302943Z", - "iopub.status.idle": "2024-09-06T19:39:23.307938Z", - "shell.execute_reply": "2024-09-06T19:39:23.307365Z" + "iopub.execute_input": "2024-09-26T14:53:24.394624Z", + "iopub.status.busy": "2024-09-26T14:53:24.394340Z", + "iopub.status.idle": "2024-09-26T14:53:24.399279Z", + "shell.execute_reply": "2024-09-26T14:53:24.398789Z" }, "nbsphinx": "hidden" }, @@ -696,10 +688,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.310122Z", - "iopub.status.busy": "2024-09-06T19:39:23.309822Z", - "iopub.status.idle": "2024-09-06T19:39:23.850296Z", - "shell.execute_reply": "2024-09-06T19:39:23.849793Z" + "iopub.execute_input": "2024-09-26T14:53:24.400938Z", + "iopub.status.busy": "2024-09-26T14:53:24.400609Z", + "iopub.status.idle": "2024-09-26T14:53:24.953810Z", + "shell.execute_reply": "2024-09-26T14:53:24.953168Z" } }, "outputs": [ @@ -732,10 +724,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.852466Z", - "iopub.status.busy": "2024-09-06T19:39:23.852115Z", - "iopub.status.idle": "2024-09-06T19:39:24.358610Z", - "shell.execute_reply": "2024-09-06T19:39:24.358030Z" + "iopub.execute_input": "2024-09-26T14:53:24.955849Z", + "iopub.status.busy": "2024-09-26T14:53:24.955452Z", + "iopub.status.idle": "2024-09-26T14:53:25.472907Z", + "shell.execute_reply": "2024-09-26T14:53:25.472351Z" } }, "outputs": [ @@ -773,10 +765,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:24.360839Z", - "iopub.status.busy": "2024-09-06T19:39:24.360464Z", - "iopub.status.idle": "2024-09-06T19:39:24.363781Z", - "shell.execute_reply": "2024-09-06T19:39:24.363295Z" + "iopub.execute_input": "2024-09-26T14:53:25.474962Z", + "iopub.status.busy": "2024-09-26T14:53:25.474606Z", + "iopub.status.idle": "2024-09-26T14:53:25.478282Z", + "shell.execute_reply": "2024-09-26T14:53:25.477855Z" } }, "outputs": [], @@ -799,17 +791,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:24.365783Z", - "iopub.status.busy": "2024-09-06T19:39:24.365442Z", - "iopub.status.idle": "2024-09-06T19:39:36.716347Z", - "shell.execute_reply": "2024-09-06T19:39:36.715721Z" + "iopub.execute_input": "2024-09-26T14:53:25.479985Z", + "iopub.status.busy": "2024-09-26T14:53:25.479646Z", + "iopub.status.idle": "2024-09-26T14:53:38.119311Z", + "shell.execute_reply": "2024-09-26T14:53:38.118760Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ceaa047f5ed4611b974d3fa414e2507", + "model_id": "502208beacbc4eb2877f50728ccb04c0", "version_major": 2, "version_minor": 0 }, @@ -868,10 +860,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:36.718898Z", - "iopub.status.busy": "2024-09-06T19:39:36.718487Z", - "iopub.status.idle": "2024-09-06T19:39:38.825920Z", - "shell.execute_reply": "2024-09-06T19:39:38.825316Z" + "iopub.execute_input": "2024-09-26T14:53:38.121453Z", + "iopub.status.busy": "2024-09-26T14:53:38.121019Z", + "iopub.status.idle": "2024-09-26T14:53:40.226608Z", + "shell.execute_reply": "2024-09-26T14:53:40.226078Z" } }, "outputs": [ @@ -915,10 +907,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:38.828812Z", - "iopub.status.busy": "2024-09-06T19:39:38.828333Z", - "iopub.status.idle": "2024-09-06T19:39:39.084401Z", - "shell.execute_reply": "2024-09-06T19:39:39.083812Z" + "iopub.execute_input": "2024-09-26T14:53:40.228769Z", + "iopub.status.busy": "2024-09-26T14:53:40.228334Z", + "iopub.status.idle": "2024-09-26T14:53:40.460757Z", + "shell.execute_reply": "2024-09-26T14:53:40.459979Z" } }, "outputs": [ @@ -954,10 +946,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:39.087122Z", - "iopub.status.busy": "2024-09-06T19:39:39.086611Z", - "iopub.status.idle": "2024-09-06T19:39:39.754107Z", - "shell.execute_reply": "2024-09-06T19:39:39.753534Z" + "iopub.execute_input": "2024-09-26T14:53:40.462963Z", + "iopub.status.busy": "2024-09-26T14:53:40.462510Z", + "iopub.status.idle": "2024-09-26T14:53:41.139530Z", + "shell.execute_reply": "2024-09-26T14:53:41.138920Z" } }, "outputs": [ @@ -1007,10 +999,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:39.756937Z", - "iopub.status.busy": "2024-09-06T19:39:39.756623Z", - "iopub.status.idle": "2024-09-06T19:39:40.092242Z", - "shell.execute_reply": "2024-09-06T19:39:40.091655Z" + "iopub.execute_input": "2024-09-26T14:53:41.141576Z", + "iopub.status.busy": "2024-09-26T14:53:41.141387Z", + "iopub.status.idle": "2024-09-26T14:53:41.442674Z", + "shell.execute_reply": "2024-09-26T14:53:41.442054Z" } }, "outputs": [ @@ -1058,10 +1050,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.094221Z", - "iopub.status.busy": "2024-09-06T19:39:40.094058Z", - "iopub.status.idle": "2024-09-06T19:39:40.335215Z", - "shell.execute_reply": "2024-09-06T19:39:40.334660Z" + "iopub.execute_input": "2024-09-26T14:53:41.444606Z", + "iopub.status.busy": "2024-09-26T14:53:41.444407Z", + "iopub.status.idle": "2024-09-26T14:53:41.692450Z", + "shell.execute_reply": "2024-09-26T14:53:41.691834Z" } }, "outputs": [ @@ -1117,10 +1109,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.337846Z", - "iopub.status.busy": "2024-09-06T19:39:40.337645Z", - "iopub.status.idle": "2024-09-06T19:39:40.434888Z", - "shell.execute_reply": "2024-09-06T19:39:40.434380Z" + "iopub.execute_input": "2024-09-26T14:53:41.694792Z", + "iopub.status.busy": "2024-09-26T14:53:41.694309Z", + "iopub.status.idle": "2024-09-26T14:53:41.786453Z", + "shell.execute_reply": "2024-09-26T14:53:41.785871Z" } }, "outputs": [], @@ -1141,10 +1133,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.437135Z", - "iopub.status.busy": "2024-09-06T19:39:40.436969Z", - "iopub.status.idle": "2024-09-06T19:39:50.846992Z", - "shell.execute_reply": "2024-09-06T19:39:50.846365Z" + "iopub.execute_input": "2024-09-26T14:53:41.788692Z", + "iopub.status.busy": "2024-09-26T14:53:41.788289Z", + "iopub.status.idle": "2024-09-26T14:53:52.391383Z", + "shell.execute_reply": "2024-09-26T14:53:52.390803Z" } }, "outputs": [ @@ -1181,10 +1173,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:50.849274Z", - "iopub.status.busy": "2024-09-06T19:39:50.849079Z", - "iopub.status.idle": "2024-09-06T19:39:53.085840Z", - "shell.execute_reply": "2024-09-06T19:39:53.085209Z" + "iopub.execute_input": "2024-09-26T14:53:52.393513Z", + "iopub.status.busy": "2024-09-26T14:53:52.393049Z", + "iopub.status.idle": "2024-09-26T14:53:54.671283Z", + "shell.execute_reply": "2024-09-26T14:53:54.670780Z" } }, "outputs": [ @@ -1215,10 +1207,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.088386Z", - "iopub.status.busy": "2024-09-06T19:39:53.087986Z", - "iopub.status.idle": "2024-09-06T19:39:53.295938Z", - "shell.execute_reply": "2024-09-06T19:39:53.295309Z" + "iopub.execute_input": "2024-09-26T14:53:54.673751Z", + "iopub.status.busy": "2024-09-26T14:53:54.673100Z", + "iopub.status.idle": "2024-09-26T14:53:54.874229Z", + "shell.execute_reply": "2024-09-26T14:53:54.873718Z" } }, "outputs": [], @@ -1232,10 +1224,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.298578Z", - "iopub.status.busy": "2024-09-06T19:39:53.298149Z", - "iopub.status.idle": "2024-09-06T19:39:53.301396Z", - "shell.execute_reply": "2024-09-06T19:39:53.300847Z" + "iopub.execute_input": "2024-09-26T14:53:54.876098Z", + "iopub.status.busy": "2024-09-26T14:53:54.875918Z", + "iopub.status.idle": "2024-09-26T14:53:54.879013Z", + "shell.execute_reply": "2024-09-26T14:53:54.878602Z" } }, "outputs": [], @@ -1273,10 +1265,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.303545Z", - "iopub.status.busy": "2024-09-06T19:39:53.303235Z", - "iopub.status.idle": "2024-09-06T19:39:53.311553Z", - "shell.execute_reply": "2024-09-06T19:39:53.311013Z" + "iopub.execute_input": "2024-09-26T14:53:54.880796Z", + "iopub.status.busy": "2024-09-26T14:53:54.880464Z", + "iopub.status.idle": "2024-09-26T14:53:54.888465Z", + "shell.execute_reply": "2024-09-26T14:53:54.888011Z" }, "nbsphinx": "hidden" }, @@ -1316,12 +1308,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "2a68a2d432424faba9fe0b5e6944b5e9": { + "19f8d7cfcb2441f39ec909950206b100": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1374,47 +1366,30 @@ "width": null } }, - "3ceaa047f5ed4611b974d3fa414e2507": { + "3dbde950338e4819980320793264b8f6": { "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_c213a022f9994559b2b3155f2f77656c", - "IPY_MODEL_d04af2b6417a48e88c2bb6ac7a1a352f", - "IPY_MODEL_d330cb5a3ec245d28c20140821dff479" - ], - "layout": "IPY_MODEL_8965ea1fe0204e49bbde2ee4ed6b5dbe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_444a8341757540238acd548381d3cf78", + "placeholder": "​", + "style": "IPY_MODEL_c63ffa48637c4cf790d73142dcbf1bca", "tabbable": null, - "tooltip": null - } - }, - "653de3cf6239488fa0adf55f2a1ae049": { - "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": "model.safetensors: 100%" } }, - "8965ea1fe0204e49bbde2ee4ed6b5dbe": { + "444a8341757540238acd548381d3cf78": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1467,7 +1442,91 @@ "width": null } }, - "b06f361a24974d5a8b8c89476e47f817": { + "4b5509bd08094575af9bfd6e1b39af74": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "502208beacbc4eb2877f50728ccb04c0": { + "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_3dbde950338e4819980320793264b8f6", + "IPY_MODEL_55906b2e5cc7451a90a629cf8eaf9dfa", + "IPY_MODEL_e08a4a5cd5a34519a67999d955a20b6a" + ], + "layout": "IPY_MODEL_f0529d443cdc4ef783433718b133c35d", + "tabbable": null, + "tooltip": null + } + }, + "55906b2e5cc7451a90a629cf8eaf9dfa": { + "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_a6577ec2ef7f4efc9edc61cb0c210c81", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6428955d0d764798b409d0eed1cd24c0", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "6428955d0d764798b409d0eed1cd24c0": { + "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": "" + } + }, + "a6577ec2ef7f4efc9edc61cb0c210c81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1520,7 +1579,7 @@ "width": null } }, - "b895588a207f4f0ca89d7c4764c3d066": { + "c63ffa48637c4cf790d73142dcbf1bca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1538,7 +1597,7 @@ "text_color": null } }, - "c213a022f9994559b2b3155f2f77656c": { + "e08a4a5cd5a34519a67999d955a20b6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1553,15 +1612,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ccda1205dcc748f99c76cf1800b182ef", + "layout": "IPY_MODEL_19f8d7cfcb2441f39ec909950206b100", "placeholder": "​", - "style": "IPY_MODEL_b895588a207f4f0ca89d7c4764c3d066", + "style": "IPY_MODEL_4b5509bd08094575af9bfd6e1b39af74", "tabbable": null, "tooltip": null, - "value": "model.safetensors: 100%" + "value": " 102M/102M [00:00<00:00, 297MB/s]" } }, - "ccda1205dcc748f99c76cf1800b182ef": { + "f0529d443cdc4ef783433718b133c35d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1613,73 +1672,6 @@ "visibility": null, "width": null } - }, - "cd03cf3d325849b9a2597fce8db90de1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d04af2b6417a48e88c2bb6ac7a1a352f": { - "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_2a68a2d432424faba9fe0b5e6944b5e9", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_653de3cf6239488fa0adf55f2a1ae049", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "d330cb5a3ec245d28c20140821dff479": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b06f361a24974d5a8b8c89476e47f817", - "placeholder": "​", - "style": "IPY_MODEL_cd03cf3d325849b9a2597fce8db90de1", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 304MB/s]" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 4e72a9c31..5670e5e42 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": "2024-09-06T19:39:57.671183Z", - "iopub.status.busy": "2024-09-06T19:39:57.671012Z", - "iopub.status.idle": "2024-09-06T19:39:58.889426Z", - "shell.execute_reply": "2024-09-06T19:39:58.888863Z" + "iopub.execute_input": "2024-09-26T14:53:59.188556Z", + "iopub.status.busy": "2024-09-26T14:53:59.188370Z", + "iopub.status.idle": "2024-09-26T14:54:00.464944Z", + "shell.execute_reply": "2024-09-26T14:54:00.464378Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:58.892009Z", - "iopub.status.busy": "2024-09-06T19:39:58.891558Z", - "iopub.status.idle": "2024-09-06T19:39:58.909420Z", - "shell.execute_reply": "2024-09-06T19:39:58.908966Z" + "iopub.execute_input": "2024-09-26T14:54:00.467202Z", + "iopub.status.busy": "2024-09-26T14:54:00.466665Z", + "iopub.status.idle": "2024-09-26T14:54:00.486020Z", + "shell.execute_reply": "2024-09-26T14:54:00.485402Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:58.911380Z", - "iopub.status.busy": "2024-09-06T19:39:58.911122Z", - "iopub.status.idle": "2024-09-06T19:39:58.914071Z", - "shell.execute_reply": "2024-09-06T19:39:58.913630Z" + "iopub.execute_input": "2024-09-26T14:54:00.488158Z", + "iopub.status.busy": "2024-09-26T14:54:00.487625Z", + "iopub.status.idle": "2024-09-26T14:54:00.490770Z", + "shell.execute_reply": "2024-09-26T14:54:00.490324Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:58.916066Z", - "iopub.status.busy": "2024-09-06T19:39:58.915883Z", - "iopub.status.idle": "2024-09-06T19:39:59.147435Z", - "shell.execute_reply": "2024-09-06T19:39:59.146903Z" + "iopub.execute_input": "2024-09-26T14:54:00.492476Z", + "iopub.status.busy": "2024-09-26T14:54:00.492170Z", + "iopub.status.idle": "2024-09-26T14:54:00.593026Z", + "shell.execute_reply": "2024-09-26T14:54:00.592503Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.149566Z", - "iopub.status.busy": "2024-09-06T19:39:59.149370Z", - "iopub.status.idle": "2024-09-06T19:39:59.331007Z", - "shell.execute_reply": "2024-09-06T19:39:59.330438Z" + "iopub.execute_input": "2024-09-26T14:54:00.595033Z", + "iopub.status.busy": "2024-09-26T14:54:00.594676Z", + "iopub.status.idle": "2024-09-26T14:54:00.781165Z", + "shell.execute_reply": "2024-09-26T14:54:00.780607Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.333486Z", - "iopub.status.busy": "2024-09-06T19:39:59.333040Z", - "iopub.status.idle": "2024-09-06T19:39:59.576590Z", - "shell.execute_reply": "2024-09-06T19:39:59.575968Z" + "iopub.execute_input": "2024-09-26T14:54:00.783347Z", + "iopub.status.busy": "2024-09-26T14:54:00.782969Z", + "iopub.status.idle": "2024-09-26T14:54:01.032458Z", + "shell.execute_reply": "2024-09-26T14:54:01.031929Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.578938Z", - "iopub.status.busy": "2024-09-06T19:39:59.578553Z", - "iopub.status.idle": "2024-09-06T19:39:59.582923Z", - "shell.execute_reply": "2024-09-06T19:39:59.582473Z" + "iopub.execute_input": "2024-09-26T14:54:01.034452Z", + "iopub.status.busy": "2024-09-26T14:54:01.034056Z", + "iopub.status.idle": "2024-09-26T14:54:01.038763Z", + "shell.execute_reply": "2024-09-26T14:54:01.038275Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.584759Z", - "iopub.status.busy": "2024-09-06T19:39:59.584580Z", - "iopub.status.idle": "2024-09-06T19:39:59.590790Z", - "shell.execute_reply": "2024-09-06T19:39:59.590351Z" + "iopub.execute_input": "2024-09-26T14:54:01.040507Z", + "iopub.status.busy": "2024-09-26T14:54:01.040163Z", + "iopub.status.idle": "2024-09-26T14:54:01.046197Z", + "shell.execute_reply": "2024-09-26T14:54:01.045737Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.592686Z", - "iopub.status.busy": "2024-09-06T19:39:59.592515Z", - "iopub.status.idle": "2024-09-06T19:39:59.595225Z", - "shell.execute_reply": "2024-09-06T19:39:59.594766Z" + "iopub.execute_input": "2024-09-26T14:54:01.048092Z", + "iopub.status.busy": "2024-09-26T14:54:01.047754Z", + "iopub.status.idle": "2024-09-26T14:54:01.050568Z", + "shell.execute_reply": "2024-09-26T14:54:01.050000Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.597032Z", - "iopub.status.busy": "2024-09-06T19:39:59.596865Z", - "iopub.status.idle": "2024-09-06T19:40:08.597697Z", - "shell.execute_reply": "2024-09-06T19:40:08.597120Z" + "iopub.execute_input": "2024-09-26T14:54:01.052488Z", + "iopub.status.busy": "2024-09-26T14:54:01.052092Z", + "iopub.status.idle": "2024-09-26T14:54:10.157589Z", + "shell.execute_reply": "2024-09-26T14:54:10.157001Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.600635Z", - "iopub.status.busy": "2024-09-06T19:40:08.599991Z", - "iopub.status.idle": "2024-09-06T19:40:08.607726Z", - "shell.execute_reply": "2024-09-06T19:40:08.607259Z" + "iopub.execute_input": "2024-09-26T14:54:10.160258Z", + "iopub.status.busy": "2024-09-26T14:54:10.159589Z", + "iopub.status.idle": "2024-09-26T14:54:10.167515Z", + "shell.execute_reply": "2024-09-26T14:54:10.167054Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.609816Z", - "iopub.status.busy": "2024-09-06T19:40:08.609470Z", - "iopub.status.idle": "2024-09-06T19:40:08.613036Z", - "shell.execute_reply": "2024-09-06T19:40:08.612542Z" + "iopub.execute_input": "2024-09-26T14:54:10.169285Z", + "iopub.status.busy": "2024-09-26T14:54:10.168935Z", + "iopub.status.idle": "2024-09-26T14:54:10.172611Z", + "shell.execute_reply": "2024-09-26T14:54:10.172168Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.615042Z", - "iopub.status.busy": "2024-09-06T19:40:08.614643Z", - "iopub.status.idle": "2024-09-06T19:40:08.618056Z", - "shell.execute_reply": "2024-09-06T19:40:08.617486Z" + "iopub.execute_input": "2024-09-26T14:54:10.174288Z", + "iopub.status.busy": "2024-09-26T14:54:10.173947Z", + "iopub.status.idle": "2024-09-26T14:54:10.177369Z", + "shell.execute_reply": "2024-09-26T14:54:10.176897Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.620104Z", - "iopub.status.busy": "2024-09-06T19:40:08.619791Z", - "iopub.status.idle": "2024-09-06T19:40:08.622907Z", - "shell.execute_reply": "2024-09-06T19:40:08.622416Z" + "iopub.execute_input": "2024-09-26T14:54:10.179183Z", + "iopub.status.busy": "2024-09-26T14:54:10.178849Z", + "iopub.status.idle": "2024-09-26T14:54:10.182081Z", + "shell.execute_reply": "2024-09-26T14:54:10.181652Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.624768Z", - "iopub.status.busy": "2024-09-06T19:40:08.624594Z", - "iopub.status.idle": "2024-09-06T19:40:08.632747Z", - "shell.execute_reply": "2024-09-06T19:40:08.632288Z" + "iopub.execute_input": "2024-09-26T14:54:10.183707Z", + "iopub.status.busy": "2024-09-26T14:54:10.183367Z", + "iopub.status.idle": "2024-09-26T14:54:10.191340Z", + "shell.execute_reply": "2024-09-26T14:54:10.190898Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.634564Z", - "iopub.status.busy": "2024-09-06T19:40:08.634392Z", - "iopub.status.idle": "2024-09-06T19:40:08.637116Z", - "shell.execute_reply": "2024-09-06T19:40:08.636642Z" + "iopub.execute_input": "2024-09-26T14:54:10.193003Z", + "iopub.status.busy": "2024-09-26T14:54:10.192665Z", + "iopub.status.idle": "2024-09-26T14:54:10.195213Z", + "shell.execute_reply": "2024-09-26T14:54:10.194766Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.639192Z", - "iopub.status.busy": "2024-09-06T19:40:08.638877Z", - "iopub.status.idle": "2024-09-06T19:40:08.766647Z", - "shell.execute_reply": "2024-09-06T19:40:08.765685Z" + "iopub.execute_input": "2024-09-26T14:54:10.196853Z", + "iopub.status.busy": "2024-09-26T14:54:10.196518Z", + "iopub.status.idle": "2024-09-26T14:54:10.322626Z", + "shell.execute_reply": "2024-09-26T14:54:10.322078Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.769173Z", - "iopub.status.busy": "2024-09-06T19:40:08.768972Z", - "iopub.status.idle": "2024-09-06T19:40:08.878186Z", - "shell.execute_reply": "2024-09-06T19:40:08.877593Z" + "iopub.execute_input": "2024-09-26T14:54:10.324771Z", + "iopub.status.busy": "2024-09-26T14:54:10.324359Z", + "iopub.status.idle": "2024-09-26T14:54:10.435194Z", + "shell.execute_reply": "2024-09-26T14:54:10.434642Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.880641Z", - "iopub.status.busy": "2024-09-06T19:40:08.880289Z", - "iopub.status.idle": "2024-09-06T19:40:09.386974Z", - "shell.execute_reply": "2024-09-06T19:40:09.386324Z" + "iopub.execute_input": "2024-09-26T14:54:10.437396Z", + "iopub.status.busy": "2024-09-26T14:54:10.436936Z", + "iopub.status.idle": "2024-09-26T14:54:10.943293Z", + "shell.execute_reply": "2024-09-26T14:54:10.942658Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.389675Z", - "iopub.status.busy": "2024-09-06T19:40:09.389308Z", - "iopub.status.idle": "2024-09-06T19:40:09.485553Z", - "shell.execute_reply": "2024-09-06T19:40:09.484996Z" + "iopub.execute_input": "2024-09-26T14:54:10.945562Z", + "iopub.status.busy": "2024-09-26T14:54:10.945188Z", + "iopub.status.idle": "2024-09-26T14:54:11.045547Z", + "shell.execute_reply": "2024-09-26T14:54:11.044913Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.487964Z", - "iopub.status.busy": "2024-09-06T19:40:09.487496Z", - "iopub.status.idle": "2024-09-06T19:40:09.496128Z", - "shell.execute_reply": "2024-09-06T19:40:09.495570Z" + "iopub.execute_input": "2024-09-26T14:54:11.047649Z", + "iopub.status.busy": "2024-09-26T14:54:11.047228Z", + "iopub.status.idle": "2024-09-26T14:54:11.055699Z", + "shell.execute_reply": "2024-09-26T14:54:11.055230Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.498303Z", - "iopub.status.busy": "2024-09-06T19:40:09.497989Z", - "iopub.status.idle": "2024-09-06T19:40:09.500756Z", - "shell.execute_reply": "2024-09-06T19:40:09.500274Z" + "iopub.execute_input": "2024-09-26T14:54:11.057456Z", + "iopub.status.busy": "2024-09-26T14:54:11.057092Z", + "iopub.status.idle": "2024-09-26T14:54:11.059706Z", + "shell.execute_reply": "2024-09-26T14:54:11.059257Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.502626Z", - "iopub.status.busy": "2024-09-06T19:40:09.502453Z", - "iopub.status.idle": "2024-09-06T19:40:15.134668Z", - "shell.execute_reply": "2024-09-06T19:40:15.134055Z" + "iopub.execute_input": "2024-09-26T14:54:11.061497Z", + "iopub.status.busy": "2024-09-26T14:54:11.061113Z", + "iopub.status.idle": "2024-09-26T14:54:16.702766Z", + "shell.execute_reply": "2024-09-26T14:54:16.702139Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:15.137003Z", - "iopub.status.busy": "2024-09-06T19:40:15.136794Z", - "iopub.status.idle": "2024-09-06T19:40:15.145626Z", - "shell.execute_reply": "2024-09-06T19:40:15.145149Z" + "iopub.execute_input": "2024-09-26T14:54:16.704653Z", + "iopub.status.busy": "2024-09-26T14:54:16.704460Z", + "iopub.status.idle": "2024-09-26T14:54:16.712980Z", + "shell.execute_reply": "2024-09-26T14:54:16.712530Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:15.147739Z", - "iopub.status.busy": "2024-09-06T19:40:15.147560Z", - "iopub.status.idle": "2024-09-06T19:40:15.212105Z", - "shell.execute_reply": "2024-09-06T19:40:15.211592Z" + "iopub.execute_input": "2024-09-26T14:54:16.714897Z", + "iopub.status.busy": "2024-09-26T14:54:16.714556Z", + "iopub.status.idle": "2024-09-26T14:54:16.786785Z", + "shell.execute_reply": "2024-09-26T14:54:16.786234Z" }, "nbsphinx": "hidden" }, @@ -1452,7 +1452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 3d1ba85ed..6779478cb 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": "2024-09-06T19:40:18.378801Z", - "iopub.status.busy": "2024-09-06T19:40:18.378438Z", - "iopub.status.idle": "2024-09-06T19:40:21.013953Z", - "shell.execute_reply": "2024-09-06T19:40:21.013191Z" + "iopub.execute_input": "2024-09-26T14:54:20.095591Z", + "iopub.status.busy": "2024-09-26T14:54:20.095416Z", + "iopub.status.idle": "2024-09-26T14:54:23.030061Z", + "shell.execute_reply": "2024-09-26T14:54:23.029312Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:21.016497Z", - "iopub.status.busy": "2024-09-06T19:40:21.016297Z", - "iopub.status.idle": "2024-09-06T19:41:26.205588Z", - "shell.execute_reply": "2024-09-06T19:41:26.204905Z" + "iopub.execute_input": "2024-09-26T14:54:23.032402Z", + "iopub.status.busy": "2024-09-26T14:54:23.032024Z", + "iopub.status.idle": "2024-09-26T14:55:28.921952Z", + "shell.execute_reply": "2024-09-26T14:55:28.921155Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:26.208261Z", - "iopub.status.busy": "2024-09-06T19:41:26.207954Z", - "iopub.status.idle": "2024-09-06T19:41:27.363762Z", - "shell.execute_reply": "2024-09-06T19:41:27.363213Z" + "iopub.execute_input": "2024-09-26T14:55:28.924172Z", + "iopub.status.busy": "2024-09-26T14:55:28.923971Z", + "iopub.status.idle": "2024-09-26T14:55:30.137143Z", + "shell.execute_reply": "2024-09-26T14:55:30.136538Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:41:27.366273Z", - "iopub.status.busy": "2024-09-06T19:41:27.365850Z", - "iopub.status.idle": "2024-09-06T19:41:27.369197Z", - "shell.execute_reply": "2024-09-06T19:41:27.368626Z" + "iopub.execute_input": "2024-09-26T14:55:30.139396Z", + "iopub.status.busy": "2024-09-26T14:55:30.139106Z", + "iopub.status.idle": "2024-09-26T14:55:30.142481Z", + "shell.execute_reply": "2024-09-26T14:55:30.141914Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.371272Z", - "iopub.status.busy": "2024-09-06T19:41:27.370943Z", - "iopub.status.idle": "2024-09-06T19:41:27.374872Z", - "shell.execute_reply": "2024-09-06T19:41:27.374336Z" + "iopub.execute_input": "2024-09-26T14:55:30.144228Z", + "iopub.status.busy": "2024-09-26T14:55:30.144050Z", + "iopub.status.idle": "2024-09-26T14:55:30.147926Z", + "shell.execute_reply": "2024-09-26T14:55:30.147419Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.377058Z", - "iopub.status.busy": "2024-09-06T19:41:27.376708Z", - "iopub.status.idle": "2024-09-06T19:41:27.380273Z", - "shell.execute_reply": "2024-09-06T19:41:27.379824Z" + "iopub.execute_input": "2024-09-26T14:55:30.149873Z", + "iopub.status.busy": "2024-09-26T14:55:30.149499Z", + "iopub.status.idle": "2024-09-26T14:55:30.153440Z", + "shell.execute_reply": "2024-09-26T14:55:30.152904Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.382286Z", - "iopub.status.busy": "2024-09-06T19:41:27.381955Z", - "iopub.status.idle": "2024-09-06T19:41:27.384835Z", - "shell.execute_reply": "2024-09-06T19:41:27.384366Z" + "iopub.execute_input": "2024-09-26T14:55:30.155269Z", + "iopub.status.busy": "2024-09-26T14:55:30.154919Z", + "iopub.status.idle": "2024-09-26T14:55:30.158026Z", + "shell.execute_reply": "2024-09-26T14:55:30.157441Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.386838Z", - "iopub.status.busy": "2024-09-06T19:41:27.386506Z", - "iopub.status.idle": "2024-09-06T19:42:04.890778Z", - "shell.execute_reply": "2024-09-06T19:42:04.890135Z" + "iopub.execute_input": "2024-09-26T14:55:30.159991Z", + "iopub.status.busy": "2024-09-26T14:55:30.159527Z", + "iopub.status.idle": "2024-09-26T14:56:07.853263Z", + "shell.execute_reply": "2024-09-26T14:56:07.852683Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00ec60662f03441f8733d768775a0ed1", + "model_id": "0d3c194b71ae41699ecaf593bb466ee6", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af401850ebaa408dae00a90bb34bc54a", + "model_id": "f246aefc67174f658fc6990471fd838b", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:04.893407Z", - "iopub.status.busy": "2024-09-06T19:42:04.893064Z", - "iopub.status.idle": "2024-09-06T19:42:05.569760Z", - "shell.execute_reply": "2024-09-06T19:42:05.569193Z" + "iopub.execute_input": "2024-09-26T14:56:07.855727Z", + "iopub.status.busy": "2024-09-26T14:56:07.855280Z", + "iopub.status.idle": "2024-09-26T14:56:08.539218Z", + "shell.execute_reply": "2024-09-26T14:56:08.538732Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:05.572221Z", - "iopub.status.busy": "2024-09-06T19:42:05.571699Z", - "iopub.status.idle": "2024-09-06T19:42:08.487750Z", - "shell.execute_reply": "2024-09-06T19:42:08.487151Z" + "iopub.execute_input": "2024-09-26T14:56:08.541245Z", + "iopub.status.busy": "2024-09-26T14:56:08.540794Z", + "iopub.status.idle": "2024-09-26T14:56:11.382690Z", + "shell.execute_reply": "2024-09-26T14:56:11.382214Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:08.490015Z", - "iopub.status.busy": "2024-09-06T19:42:08.489812Z", - "iopub.status.idle": "2024-09-06T19:42:42.122207Z", - "shell.execute_reply": "2024-09-06T19:42:42.121639Z" + "iopub.execute_input": "2024-09-26T14:56:11.384692Z", + "iopub.status.busy": "2024-09-26T14:56:11.384341Z", + "iopub.status.idle": "2024-09-26T14:56:43.908330Z", + "shell.execute_reply": "2024-09-26T14:56:43.907746Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8ef1d6ee6da4d52bd3aa4ef30d9915f", + "model_id": "b6f8c999233c44e6b60c123e18607ca1", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:42.124501Z", - "iopub.status.busy": "2024-09-06T19:42:42.124158Z", - "iopub.status.idle": "2024-09-06T19:42:57.234866Z", - "shell.execute_reply": "2024-09-06T19:42:57.234293Z" + "iopub.execute_input": "2024-09-26T14:56:43.910328Z", + "iopub.status.busy": "2024-09-26T14:56:43.909998Z", + "iopub.status.idle": "2024-09-26T14:56:59.179852Z", + "shell.execute_reply": "2024-09-26T14:56:59.179195Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:57.237390Z", - "iopub.status.busy": "2024-09-06T19:42:57.237016Z", - "iopub.status.idle": "2024-09-06T19:43:00.971913Z", - "shell.execute_reply": "2024-09-06T19:43:00.971312Z" + "iopub.execute_input": "2024-09-26T14:56:59.182094Z", + "iopub.status.busy": "2024-09-26T14:56:59.181790Z", + "iopub.status.idle": "2024-09-26T14:57:03.058054Z", + "shell.execute_reply": "2024-09-26T14:57:03.057557Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:00.974009Z", - "iopub.status.busy": "2024-09-06T19:43:00.973827Z", - "iopub.status.idle": "2024-09-06T19:43:02.404764Z", - "shell.execute_reply": "2024-09-06T19:43:02.404239Z" + "iopub.execute_input": "2024-09-26T14:57:03.059722Z", + "iopub.status.busy": "2024-09-26T14:57:03.059541Z", + "iopub.status.idle": "2024-09-26T14:57:04.551144Z", + "shell.execute_reply": "2024-09-26T14:57:04.550565Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33547ea19ce34215b8f9bbd75c870924", + "model_id": "5444a2dc1c4c403ab396248114105df7", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:02.407222Z", - "iopub.status.busy": "2024-09-06T19:43:02.406914Z", - "iopub.status.idle": "2024-09-06T19:43:02.435740Z", - "shell.execute_reply": "2024-09-06T19:43:02.435223Z" + "iopub.execute_input": "2024-09-26T14:57:04.553436Z", + "iopub.status.busy": "2024-09-26T14:57:04.552948Z", + "iopub.status.idle": "2024-09-26T14:57:04.584746Z", + "shell.execute_reply": "2024-09-26T14:57:04.584077Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:02.438408Z", - "iopub.status.busy": "2024-09-06T19:43:02.438030Z", - "iopub.status.idle": "2024-09-06T19:43:08.523002Z", - "shell.execute_reply": "2024-09-06T19:43:08.522439Z" + "iopub.execute_input": "2024-09-26T14:57:04.587085Z", + "iopub.status.busy": "2024-09-26T14:57:04.586688Z", + "iopub.status.idle": "2024-09-26T14:57:10.717190Z", + "shell.execute_reply": "2024-09-26T14:57:10.716706Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:08.525189Z", - "iopub.status.busy": "2024-09-06T19:43:08.524868Z", - "iopub.status.idle": "2024-09-06T19:43:08.580916Z", - "shell.execute_reply": "2024-09-06T19:43:08.580242Z" + "iopub.execute_input": "2024-09-26T14:57:10.719179Z", + "iopub.status.busy": "2024-09-26T14:57:10.718830Z", + "iopub.status.idle": "2024-09-26T14:57:10.774462Z", + "shell.execute_reply": "2024-09-26T14:57:10.773892Z" }, "nbsphinx": "hidden" }, @@ -1033,36 +1033,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "00ec60662f03441f8733d768775a0ed1": { + "008823ad1c554e5fa5b1815e6e7eee3a": { "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_42d03e2415284486b25cf67ccd387444", - "IPY_MODEL_5050585031b24c079460a52a9a4fc488", - "IPY_MODEL_b1848abf52f742ed9f7657ba08af06f7" - ], - "layout": "IPY_MODEL_d73b0ac161c9411fb176d09cfe007d5d", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0555e6f1fc524e749446c0929d265eab": { + "038939be2791404a8d8b3535498c5720": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1115,7 +1109,57 @@ "width": null } }, - "08c7a2f2c6804a7da25a3555d45832fe": { + "03edd2e8077d415a86a42428227957c1": { + "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_d5f572bcf9e34ff5b3f799cfc3b2c03c", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "0d3c194b71ae41699ecaf593bb466ee6": { + "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_5d4af35c70b14d9b95542e9fbacf5ee2", + "IPY_MODEL_03edd2e8077d415a86a42428227957c1", + "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86" + ], + "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", + "tabbable": null, + "tooltip": null + } + }, + "11d7b29b91e94ee382b5c3abbb5da356": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1168,7 +1212,76 @@ "width": null } }, - "0f26c903e03a409eb8eb23a06ad068a1": { + "15a01925ca5e45e5bb086a7b185ac53c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", + "placeholder": "​", + "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:25<00:00,  1.22it/s]" + } + }, + "1b8ddda746534779bbbb5fd4b8f8df0b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", + "placeholder": "​", + "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:32<00:00, 153968.10it/s]" + } + }, + "21f930a5e16b44e6896cab16aadf76b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", + "placeholder": "​", + "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" + } + }, + "2cada3550c7444318e24a19d4c5bac92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1186,7 +1299,7 @@ "text_color": null } }, - "219cc478643a4ee5ac3bd50beeb53306": { + "2e397451daaa420aac06b58d115ddb89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1201,15 +1314,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7b66b5652e59476aab6385c55f338eaf", + "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", "placeholder": "​", - "style": "IPY_MODEL_c70c5f7b514a4120b47fe4694b8aa561", + "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.35it/s]" + "value": "100%" } }, - "23f68f6bcc9f4247ac306e707ae76a3e": { + "3539b50ce0e843448d49322ce25b2b2e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,23 +1375,104 @@ "width": null } }, - "26c9d71cd2b144f5a62f2e547396cf9d": { + "38f90d531db8409db73b7389ee4986c2": { "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 + } + }, + "414724ec89444e8ebc1105e3c21216d3": { + "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 + } + }, + "477c45c2e60a4cc7bc955c274f038c75": { + "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_496448b9dfc748d7b07ed9a700cc1ab7", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "31810f3656744673bb829bd7c19b4796": { + "496448b9dfc748d7b07ed9a700cc1ab7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1331,7 +1525,30 @@ "width": null } }, - "33547ea19ce34215b8f9bbd75c870924": { + "4db5293fd3e94b6eb261d17cfdd19337": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", + "placeholder": "​", + "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 20.31it/s]" + } + }, + "5444a2dc1c4c403ab396248114105df7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1346,16 +1563,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6901169080b04ad499942dc391b9b336", - "IPY_MODEL_90fef083c08c4c3c927458dfb8b00fe9", - "IPY_MODEL_219cc478643a4ee5ac3bd50beeb53306" + "IPY_MODEL_e15e2d1b74894e47b98ed243861d83d8", + "IPY_MODEL_db3abba05009401583103fd3bfc35643", + "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337" ], - "layout": "IPY_MODEL_571062df41e24ec2a51ede636c1c40ae", + "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", "tabbable": null, "tooltip": null } }, - "428890bcca0c4c398b4c85e7b197ef23": { + "5ab8498d427444c6b4d07bf8d5bc6157": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1371,7 +1588,7 @@ "description_width": "" } }, - "42d03e2415284486b25cf67ccd387444": { + "5d4af35c70b14d9b95542e9fbacf5ee2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1386,15 +1603,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_08c7a2f2c6804a7da25a3555d45832fe", + "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", "placeholder": "​", - "style": "IPY_MODEL_5428bca92792410db3731a76852725a2", + "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: 100%" } }, - "44a941086c164d5bb775c41c7d4ac57f": { + "60825090590c4420817f531a12ba0cb9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1447,7 +1664,7 @@ "width": null } }, - "474187191bb2423bbeaab8075807fc8d": { + "6c7533cc89b74b90bdcf06dda9d4297f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,81 +1717,14 @@ "width": null } }, - "5050585031b24c079460a52a9a4fc488": { - "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_fefad91592514c8b93cde6a9aa658432", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_26c9d71cd2b144f5a62f2e547396cf9d", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "5428bca92792410db3731a76852725a2": { - "model_module": "@jupyter-widgets/controls", + "6fc3fa5fef38489287ed8d9f7c6e1c3e": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "56086d38b6e24dd381b3d2d8adfc7dee": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8b4141a6045142c1b9ba131103d924f0", - "placeholder": "​", - "style": "IPY_MODEL_62e07c87b8f14d10ae3081dc89c264cb", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:25<00:00,  1.19it/s]" - } - }, - "571062df41e24ec2a51ede636c1c40ae": { - "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", @@ -1620,7 +1770,7 @@ "width": null } }, - "5aa47fe7e6cf4464bcbe167e6d3ba68a": { + "7211d82a11904799ba5182ef4f7e1762": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1638,92 +1788,7 @@ "text_color": null } }, - "5e211e7a482d4ffc95757eed7f7aa9cc": { - "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_71cc03f01ffb487095fef61fe310cb72", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_428890bcca0c4c398b4c85e7b197ef23", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "62e07c87b8f14d10ae3081dc89c264cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6901169080b04ad499942dc391b9b336": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fa2dd8d15728476eac598aeb95576e3b", - "placeholder": "​", - "style": "IPY_MODEL_a247c69930644302aed767d71b7ec676", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "71684d8531234f3d9d16e15f5e2a1318": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "71cc03f01ffb487095fef61fe310cb72": { + "a6ce96bd4a1f4a83b1164ac6cbe3d02f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1776,7 +1841,7 @@ "width": null } }, - "71d9c9ff1e934321985ce73f6d70432d": { + "aad869b6d41d459097999efed9f5aabb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1829,25 +1894,7 @@ "width": null } }, - "72aa2b7d62f44bfba1fef33687cd2d9c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "733932bb0ae3401390e27945e01e9afa": { + "aeabc766c99c4e2b8edffb93d948620a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1900,60 +1947,25 @@ "width": null } }, - "7b66b5652e59476aab6385c55f338eaf": { - "model_module": "@jupyter-widgets/base", + "aedea8fe506b40c1933ac0b06c3dc5c7": { + "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 } }, - "8b4141a6045142c1b9ba131103d924f0": { + "b25023ee46574f0987d4401430bdbe95": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2006,77 +2018,7 @@ "width": null } }, - "8c44f5cb10834552b9f054ccff28de8f": { - "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_0555e6f1fc524e749446c0929d265eab", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b4ff48b5ef42475cb8d931380feef05a", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 - } - }, - "90fef083c08c4c3c927458dfb8b00fe9": { - "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_d8f70224ecee42f48ecf14d646040c54", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e468d38bc9454ebf87117d355645f3f1", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "a247c69930644302aed767d71b7ec676": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a8ef1d6ee6da4d52bd3aa4ef30d9915f": { + "b6f8c999233c44e6b60c123e18607ca1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2091,109 +2033,58 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e7a051930ecf4f8da5a7114fa550bc7c", - "IPY_MODEL_8c44f5cb10834552b9f054ccff28de8f", - "IPY_MODEL_abb55722ee8a4e9383f54ba9776bfb21" + "IPY_MODEL_2e397451daaa420aac06b58d115ddb89", + "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", + "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b" ], - "layout": "IPY_MODEL_44a941086c164d5bb775c41c7d4ac57f", + "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", "tabbable": null, "tooltip": null } }, - "abb55722ee8a4e9383f54ba9776bfb21": { + "c0780c5558e44bdf9cd38943fbc6879f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_23f68f6bcc9f4247ac306e707ae76a3e", - "placeholder": "​", - "style": "IPY_MODEL_0f26c903e03a409eb8eb23a06ad068a1", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:33<00:00, 147431.29it/s]" - } - }, - "ac71e20e794944a5ad10d81bd3802d6a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_733932bb0ae3401390e27945e01e9afa", - "placeholder": "​", - "style": "IPY_MODEL_72aa2b7d62f44bfba1fef33687cd2d9c", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "af401850ebaa408dae00a90bb34bc54a": { - "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_ac71e20e794944a5ad10d81bd3802d6a", - "IPY_MODEL_5e211e7a482d4ffc95757eed7f7aa9cc", - "IPY_MODEL_56086d38b6e24dd381b3d2d8adfc7dee" - ], - "layout": "IPY_MODEL_71d9c9ff1e934321985ce73f6d70432d", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "b1848abf52f742ed9f7657ba08af06f7": { + "c126cab8ab9c4826849b4c390465afaf": { "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_474187191bb2423bbeaab8075807fc8d", - "placeholder": "​", - "style": "IPY_MODEL_5aa47fe7e6cf4464bcbe167e6d3ba68a", + "layout": "IPY_MODEL_3539b50ce0e843448d49322ce25b2b2e", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:00<00:00, 787.55it/s]" + "value": 4997683.0 } }, - "b4ff48b5ef42475cb8d931380feef05a": { + "c19fb70e50ef4b3aa215c397be2fa0ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2209,25 +2100,7 @@ "description_width": "" } }, - "c70c5f7b514a4120b47fe4694b8aa561": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d73b0ac161c9411fb176d09cfe007d5d": { + "c41365fd01984997be6e7450cfa7d4d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2280,7 +2153,7 @@ "width": null } }, - "d8f70224ecee42f48ecf14d646040c54": { + "cbe2b73a42764a2aacaeaee0b9c612b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2333,23 +2206,43 @@ "width": null } }, - "e468d38bc9454ebf87117d355645f3f1": { + "cbf3bc2871f144bda8f96df51315cc6a": { "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 } }, - "e7a051930ecf4f8da5a7114fa550bc7c": { + "d26696e0cc9f4eef935b52e6f5301e41": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d408f59a6c4642dbacacf8536dd5bb86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2364,15 +2257,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31810f3656744673bb829bd7c19b4796", + "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", "placeholder": "​", - "style": "IPY_MODEL_71684d8531234f3d9d16e15f5e2a1318", + "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 30/30 [00:00<00:00, 761.55it/s]" } }, - "fa2dd8d15728476eac598aeb95576e3b": { + "d5f572bcf9e34ff5b3f799cfc3b2c03c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2425,7 +2318,114 @@ "width": null } }, - "fefad91592514c8b93cde6a9aa658432": { + "db3abba05009401583103fd3bfc35643": { + "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_414724ec89444e8ebc1105e3c21216d3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "e15e2d1b74894e47b98ed243861d83d8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", + "placeholder": "​", + "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" + } + }, + "e7222a4d37404d41a68f2bf782915ef2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f246aefc67174f658fc6990471fd838b": { + "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_21f930a5e16b44e6896cab16aadf76b0", + "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", + "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c" + ], + "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", + "tabbable": null, + "tooltip": null + } + }, + "fc418d04bfd44dc999d29a7cfbaf1bf5": { + "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": "" + } + }, + "ffca702bd3444f1690f1f5f85493ca09": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index c988c12c2..9d0e0764f 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": "2024-09-06T19:43:11.117353Z", - "iopub.status.busy": "2024-09-06T19:43:11.117178Z", - "iopub.status.idle": "2024-09-06T19:43:13.210573Z", - "shell.execute_reply": "2024-09-06T19:43:13.209958Z" + "iopub.execute_input": "2024-09-26T14:57:13.331707Z", + "iopub.status.busy": "2024-09-26T14:57:13.331541Z", + "iopub.status.idle": "2024-09-26T14:57:15.936866Z", + "shell.execute_reply": "2024-09-26T14:57:15.936192Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:43:11-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-09-26 14:57:13-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.249.167, 2400:52e0:1a01::907:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.249.167|:443... connected.\r\n", + "185.93.1.243, 2400:52e0:1a00::940:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.243|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -118,7 +118,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-09-06 19:43:11 (7.82 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-09-26 14:57:13 (7.67 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -127,33 +127,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Archive: conll2003.zip\r\n", - " inflating: data/metadata \r\n", - " inflating: data/test.txt \r\n", - " inflating: data/train.txt \r\n", - " inflating: data/valid.txt \r\n" + "Archive: conll2003.zip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:43:11-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.201.17, 52.217.193.233, 52.217.81.84, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.201.17|:443... " + " inflating: data/metadata \r\n", + " inflating: data/test.txt \r\n", + " inflating: data/train.txt \r\n", + " inflating: data/valid.txt \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "connected.\r\n" + "--2024-09-26 14:57:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.27.119, 52.217.207.97, 52.217.171.81, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.27.119|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,7 +174,31 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 0%[ ] 142.53K 668KB/s " + "pred_probs.npz 2%[ ] 482.32K 2.17MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 7%[> ] 1.23M 2.84MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 14%[=> ] 2.42M 3.72MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 26%[====> ] 4.26M 4.90MB/s " ] }, { @@ -182,7 +206,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 8%[> ] 1.35M 3.16MB/s " + "pred_probs.npz 43%[=======> ] 7.12M 6.54MB/s " ] }, { @@ -190,7 +214,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 50%[=========> ] 8.28M 12.9MB/s " + "pred_probs.npz 71%[=============> ] 11.56M 8.85MB/s " ] }, { @@ -198,9 +222,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 20.4MB/s in 0.8s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 11.2MB/s in 1.5s \r\n", "\r\n", - "2024-09-06 19:43:13 (20.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-09-26 14:57:15 (11.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -217,10 +241,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:13.213109Z", - "iopub.status.busy": "2024-09-06T19:43:13.212725Z", - "iopub.status.idle": "2024-09-06T19:43:14.513752Z", - "shell.execute_reply": "2024-09-06T19:43:14.513226Z" + "iopub.execute_input": "2024-09-26T14:57:15.939149Z", + "iopub.status.busy": "2024-09-26T14:57:15.938782Z", + "iopub.status.idle": "2024-09-26T14:57:17.187528Z", + "shell.execute_reply": "2024-09-26T14:57:17.186884Z" }, "nbsphinx": "hidden" }, @@ -231,7 +255,7 @@ "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -257,10 +281,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.516436Z", - "iopub.status.busy": "2024-09-06T19:43:14.515941Z", - "iopub.status.idle": "2024-09-06T19:43:14.519305Z", - "shell.execute_reply": "2024-09-06T19:43:14.518871Z" + "iopub.execute_input": "2024-09-26T14:57:17.190094Z", + "iopub.status.busy": "2024-09-26T14:57:17.189576Z", + "iopub.status.idle": "2024-09-26T14:57:17.193093Z", + "shell.execute_reply": "2024-09-26T14:57:17.192623Z" } }, "outputs": [], @@ -310,10 +334,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.521508Z", - "iopub.status.busy": "2024-09-06T19:43:14.521171Z", - "iopub.status.idle": "2024-09-06T19:43:14.524052Z", - "shell.execute_reply": "2024-09-06T19:43:14.523615Z" + "iopub.execute_input": "2024-09-26T14:57:17.194944Z", + "iopub.status.busy": "2024-09-26T14:57:17.194599Z", + "iopub.status.idle": "2024-09-26T14:57:17.197554Z", + "shell.execute_reply": "2024-09-26T14:57:17.197086Z" }, "nbsphinx": "hidden" }, @@ -331,10 +355,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.526149Z", - "iopub.status.busy": "2024-09-06T19:43:14.525818Z", - "iopub.status.idle": "2024-09-06T19:43:23.627822Z", - "shell.execute_reply": "2024-09-06T19:43:23.627249Z" + "iopub.execute_input": "2024-09-26T14:57:17.199051Z", + "iopub.status.busy": "2024-09-26T14:57:17.198872Z", + "iopub.status.idle": "2024-09-26T14:57:26.446906Z", + "shell.execute_reply": "2024-09-26T14:57:26.446343Z" } }, "outputs": [], @@ -408,10 +432,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.630427Z", - "iopub.status.busy": "2024-09-06T19:43:23.630129Z", - "iopub.status.idle": "2024-09-06T19:43:23.635623Z", - "shell.execute_reply": "2024-09-06T19:43:23.635160Z" + "iopub.execute_input": "2024-09-26T14:57:26.449170Z", + "iopub.status.busy": "2024-09-26T14:57:26.448693Z", + "iopub.status.idle": "2024-09-26T14:57:26.454297Z", + "shell.execute_reply": "2024-09-26T14:57:26.453763Z" }, "nbsphinx": "hidden" }, @@ -451,10 +475,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.637682Z", - "iopub.status.busy": "2024-09-06T19:43:23.637404Z", - "iopub.status.idle": "2024-09-06T19:43:23.985761Z", - "shell.execute_reply": "2024-09-06T19:43:23.985192Z" + "iopub.execute_input": "2024-09-26T14:57:26.456078Z", + "iopub.status.busy": "2024-09-26T14:57:26.455769Z", + "iopub.status.idle": "2024-09-26T14:57:26.817319Z", + "shell.execute_reply": "2024-09-26T14:57:26.816634Z" } }, "outputs": [], @@ -491,10 +515,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.988095Z", - "iopub.status.busy": "2024-09-06T19:43:23.987906Z", - "iopub.status.idle": "2024-09-06T19:43:23.992118Z", - "shell.execute_reply": "2024-09-06T19:43:23.991556Z" + "iopub.execute_input": "2024-09-26T14:57:26.819374Z", + "iopub.status.busy": "2024-09-26T14:57:26.819176Z", + "iopub.status.idle": "2024-09-26T14:57:26.823791Z", + "shell.execute_reply": "2024-09-26T14:57:26.823316Z" } }, "outputs": [ @@ -566,10 +590,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.994018Z", - "iopub.status.busy": "2024-09-06T19:43:23.993843Z", - "iopub.status.idle": "2024-09-06T19:43:26.637725Z", - "shell.execute_reply": "2024-09-06T19:43:26.636888Z" + "iopub.execute_input": "2024-09-26T14:57:26.825588Z", + "iopub.status.busy": "2024-09-26T14:57:26.825150Z", + "iopub.status.idle": "2024-09-26T14:57:29.558927Z", + "shell.execute_reply": "2024-09-26T14:57:29.558069Z" } }, "outputs": [], @@ -591,10 +615,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.641128Z", - "iopub.status.busy": "2024-09-06T19:43:26.640324Z", - "iopub.status.idle": "2024-09-06T19:43:26.644620Z", - "shell.execute_reply": "2024-09-06T19:43:26.644038Z" + "iopub.execute_input": "2024-09-26T14:57:29.561613Z", + "iopub.status.busy": "2024-09-26T14:57:29.560961Z", + "iopub.status.idle": "2024-09-26T14:57:29.565280Z", + "shell.execute_reply": "2024-09-26T14:57:29.564687Z" } }, "outputs": [ @@ -630,10 +654,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.646963Z", - "iopub.status.busy": "2024-09-06T19:43:26.646497Z", - "iopub.status.idle": "2024-09-06T19:43:26.651999Z", - "shell.execute_reply": "2024-09-06T19:43:26.651546Z" + "iopub.execute_input": "2024-09-26T14:57:29.567105Z", + "iopub.status.busy": "2024-09-26T14:57:29.566772Z", + "iopub.status.idle": "2024-09-26T14:57:29.572163Z", + "shell.execute_reply": "2024-09-26T14:57:29.571688Z" } }, "outputs": [ @@ -811,10 +835,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.654071Z", - "iopub.status.busy": "2024-09-06T19:43:26.653731Z", - "iopub.status.idle": "2024-09-06T19:43:26.680854Z", - "shell.execute_reply": "2024-09-06T19:43:26.680272Z" + "iopub.execute_input": "2024-09-26T14:57:29.573957Z", + "iopub.status.busy": "2024-09-26T14:57:29.573552Z", + "iopub.status.idle": "2024-09-26T14:57:29.601023Z", + "shell.execute_reply": "2024-09-26T14:57:29.600416Z" } }, "outputs": [ @@ -916,10 +940,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.683063Z", - "iopub.status.busy": "2024-09-06T19:43:26.682748Z", - "iopub.status.idle": "2024-09-06T19:43:26.687165Z", - "shell.execute_reply": "2024-09-06T19:43:26.686677Z" + "iopub.execute_input": "2024-09-26T14:57:29.602952Z", + "iopub.status.busy": "2024-09-26T14:57:29.602606Z", + "iopub.status.idle": "2024-09-26T14:57:29.607644Z", + "shell.execute_reply": "2024-09-26T14:57:29.607163Z" } }, "outputs": [ @@ -993,10 +1017,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.689077Z", - "iopub.status.busy": "2024-09-06T19:43:26.688908Z", - "iopub.status.idle": "2024-09-06T19:43:28.095086Z", - "shell.execute_reply": "2024-09-06T19:43:28.094529Z" + "iopub.execute_input": "2024-09-26T14:57:29.609321Z", + "iopub.status.busy": "2024-09-26T14:57:29.608970Z", + "iopub.status.idle": "2024-09-26T14:57:31.052597Z", + "shell.execute_reply": "2024-09-26T14:57:31.052050Z" } }, "outputs": [ @@ -1168,10 +1192,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:28.097561Z", - "iopub.status.busy": "2024-09-06T19:43:28.097109Z", - "iopub.status.idle": "2024-09-06T19:43:28.101190Z", - "shell.execute_reply": "2024-09-06T19:43:28.100749Z" + "iopub.execute_input": "2024-09-26T14:57:31.054589Z", + "iopub.status.busy": "2024-09-26T14:57:31.054180Z", + "iopub.status.idle": "2024-09-26T14:57:31.058507Z", + "shell.execute_reply": "2024-09-26T14:57:31.057947Z" }, "nbsphinx": "hidden" }, @@ -1204,7 +1228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 41b7e3f4e..72bcf4a6b 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 dea8d934f..c129f1e1a 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 90395ba3d..6371237a0 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 5cea7eca7..f3ee38213 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 9474aa59a..3738cfb70 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 6ee346b21..fb5643e46 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 272a5859e..2b46e8577 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 2452b1622..4e29de852 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 a7ea38c93..bed5a7f62 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 30340024b..4d8dd8d19 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 f6534518e..7335a26c6 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 f4d9c9605..c81e7bbb3 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 ab8195074..bda516c39 100644 Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ diff --git a/master/.doctrees/tutorials/improving_ml_performance.doctree b/master/.doctrees/tutorials/improving_ml_performance.doctree index abaf10fb6..99d9fa96f 100644 Binary files a/master/.doctrees/tutorials/improving_ml_performance.doctree and b/master/.doctrees/tutorials/improving_ml_performance.doctree differ diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index 124c2e420..346ef68b8 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 433c4f517..defff680d 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 8e296d6cf..0bdcd4506 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 d09ff0b65..4719179d1 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 94f991fc8..ee2fe17cb 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 3d0d6d533..97ff7c611 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 cb12241c5..ab949e2e7 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 b0b64f5a5..5359952a4 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 9d1d72787..377482486 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 d1c6aa6d5..c1cf22ab6 100644 Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ diff --git a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst index 2e72a08fd..fe365b3fd 100644 --- a/master/_sources/cleanlab/datalab/guide/issue_type_description.rst +++ b/master/_sources/cleanlab/datalab/guide/issue_type_description.rst @@ -669,14 +669,14 @@ Outlier Issue Parameters .. code-block:: python outlier_kwargs = { - "threshold": # floating value between 0 and 1 that sets the sensitivity of the outlier detection algorithms, based on either features or pred_probs.. + "threshold": # floating value between 0 and 1 that sets the sensitivity of the outlier detection algorithms, based on either features or pred_probs. + "k": # integer representing the number of nearest neighbors for nearest neighbors search (passed as argument to `NearestNeighbors`), if necessary, Used with features, + "t": # integer used to modulate the strength of the transformation from distances to scores that lie in the range [0, 1]. Used with features, + "scaling_factor": # floating value used to normalize the distances before they are converted into scores. Used with features, + "metric": # string or callable representing the distance metric used in nearest neighbors search (passed as argument to `NearestNeighbors`), if necessary, Used with features, "ood_kwargs": # dict of keyword arguments to constructor `OutOfDistribution()`{ "params": { # NOTE: Each of the following keyword arguments can also be provided outside "ood_kwargs" - - "knn": # `knn` argument to constructor `OutOfDistribution()`. Used with features, - "k": # `k` argument to constructor `OutOfDistribution()`. Used with features, - "t": # `t` argument to constructor `OutOfDistribution()`. Used with features, "adjust_pred_probs": # `adjust_pred_probs` argument to constructor `OutOfDistribution()`. Used with pred_probs, "method": # `method` argument to constructor `OutOfDistribution()`. Used with pred_probs, "confident_thresholds": # `confident_thresholds` argument to constructor `OutOfDistribution()`. Used with pred_probs, diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index dfa986166..ddeeca8a1 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 96aa2015a..19e39730a 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 fb9f7a38a..f2ae592a5 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 1f4d94f78..09e312137 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 e9f1461fe..505cc2f20 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 5dcb0dd9f..cebc3c1c5 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 5fca24e96..6953cf6d3 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 ca90c3c59..47a83d9d9 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/improving_ml_performance.ipynb b/master/_sources/tutorials/improving_ml_performance.ipynb index d104da0e9..ef0bc1f07 100644 --- a/master/_sources/tutorials/improving_ml_performance.ipynb +++ b/master/_sources/tutorials/improving_ml_performance.ipynb @@ -67,7 +67,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 ec9b0c142..73d01f5f0 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 a0ba8e763..7853ae5ba 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 8b7654606..e12721dcd 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 155a9b7d0..a32c08dea 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 40dabd38c..2caee695d 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 b36b8a466..ed5d33d20 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 eb6cafaa0..43216f98a 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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 d3bb49df4..672c4326e 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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html index 0911e92be..3d6003272 100644 --- a/master/cleanlab/datalab/guide/issue_type_description.html +++ b/master/cleanlab/datalab/guide/issue_type_description.html @@ -1364,14 +1364,14 @@

Label Issue Parameters

Outlier Issue Parameters#

outlier_kwargs = {
-    "threshold": # floating value between 0 and 1 that sets the sensitivity of the outlier detection algorithms, based on either features or pred_probs..
+    "threshold": # floating value between 0 and 1 that sets the sensitivity of the outlier detection algorithms, based on either features or pred_probs.
+    "k": # integer representing the number of nearest neighbors for nearest neighbors search (passed as argument to `NearestNeighbors`), if necessary, Used with features,
+    "t": # integer used to modulate the strength of the transformation from distances to scores that lie in the range [0, 1]. Used with features,
+    "scaling_factor": # floating value used to normalize the distances before they are converted into scores. Used with features,
+    "metric": # string or callable representing the distance metric used in nearest neighbors search (passed as argument to `NearestNeighbors`), if necessary, Used with features,
     "ood_kwargs": # dict of keyword arguments to constructor `OutOfDistribution()`{
             "params": {
                     # NOTE: Each of the following keyword arguments can also be provided outside "ood_kwargs"
-
-                    "knn": # `knn` argument to constructor `OutOfDistribution()`. Used with features,
-                    "k": # `k` argument to constructor `OutOfDistribution()`. Used with features,
-                    "t": # `t` argument to constructor `OutOfDistribution()`. Used with features,
                     "adjust_pred_probs": # `adjust_pred_probs` argument to constructor `OutOfDistribution()`. Used with pred_probs,
                     "method": # `method` argument to constructor `OutOfDistribution()`. Used with pred_probs,
                     "confident_thresholds": # `confident_thresholds` argument to constructor `OutOfDistribution()`. Used with pred_probs,
diff --git a/master/searchindex.js b/master/searchindex.js
index 3b057ca24..38dc81ec0 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/adapter/imagelab", "cleanlab/datalab/internal/adapter/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/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/improving_ml_performance", "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/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/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/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/improving_ml_performance.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", "imagelab", "adapter", "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", "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", "Improving ML Performance via Data Curation with Train vs Test Splits", "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, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "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, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 98, 99, 100, 101, 105], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105], "680894": 10, "41": [10, 97, 98, 100, 105, 108], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "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, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "spurious_correlations_kwarg": 10, "enough": [10, 43, 59, 97, 99], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "scaling_factor": [31, 57], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "strength": [57, 71, 97], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 106, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": [88, 110], "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "change_pin": [89, 96], "visa_or_mastercard": [89, 96], "getting_spare_card": [89, 96], "supported_cards_and_curr": [89, 96], "lost_or_stolen_phon": [89, 96], "beneficiary_not_allow": [89, 96], "apple_pay_or_google_pai": [89, 96], "card_about_to_expir": [89, 96], "card_payment_fee_charg": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109, 110], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 97, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105, 110], "039122": 91, "044598": 91, "105": [91, 105], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105, 110], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105, 110], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "923": 93, "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "597": [93, 100], "493": 93, "060": 93, "922": 93, "330": [93, 100, 105], "505": 93, "912": [93, 101], "476": [93, 100], "340": [93, 100], "879": 93, "328": [93, 105], "310": 93, "556": 93, "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 97, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "733": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "999": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "523": [97, 100], "991400": 97, "356958": 97, "362": 97, "619565": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108, 110], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "111": [97, 103, 108], "153": [97, 100, 105], "110": [97, 105], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": [97, 110], "005": 97, "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105], "167": [98, 101, 105, 110], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "965": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "605": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108, 110], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "363": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101, 110], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105, 110], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": 101, "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105, 110], "174": 101, "150": [101, 103, 105, 110], "169": [101, 110], "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": 104, "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": [105, 110], "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": 105, "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": [105, 110], "237": 105, "207": 105, "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "46456493": 106, "64it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a01": 110, "907": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "inflat": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "4mb": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 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"], [18, 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.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [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.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [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.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[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.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 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, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "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"], [21, "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"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "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"]], "Spurious Correlations Issue Parameters": [[10, "spurious-correlations-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "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?": [[99, "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?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "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?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "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.": [[101, "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": [[101, "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": [[101, "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!": [[101, "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": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "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)": [[101, "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:": [[101, "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": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "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": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "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.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "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"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "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)": [[54, "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)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "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/adapter/imagelab", "cleanlab/datalab/internal/adapter/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/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/improving_ml_performance", "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/adapter/imagelab.rst", "cleanlab/datalab/internal/adapter/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/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/improving_ml_performance.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", "imagelab", "adapter", "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", "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", "Improving ML Performance via Data Curation with Train vs Test Splits", "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, 86, 91, 92, 101, 103, 104], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 91, 92, 101, 103, 104], "generate_noise_matrix_from_trac": [0, 1, 91, 92, 101, 103, 104], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 19, 43, 48, 50, 51, 52, 53, 57, 58, 59, 70, 93, 97, 98, 110], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 29, 32, 33, 35, 37, 39, 40, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 85, 86, 91, 98, 107], "benchmark": [1, 40, 85, 86, 91, 92, 101, 103, 104], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 100, 102, 107], "": [1, 2, 3, 4, 10, 21, 35, 39, 40, 44, 48, 51, 54, 56, 57, 59, 63, 64, 68, 70, 71, 72, 73, 75, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "core": [1, 43, 46, 77, 79], "algorithm": [1, 2, 8, 10, 34, 41, 45, 56, 57, 59, 63, 72, 81, 83, 85, 88, 89, 92, 95, 96, 97, 98, 99, 101, 103, 104, 106, 108, 110], "These": [1, 2, 3, 4, 5, 8, 10, 24, 40, 42, 44, 45, 46, 47, 54, 61, 63, 64, 67, 71, 72, 76, 80, 81, 83, 84, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "introduc": [1, 10, 90, 97, 99, 100, 101], "synthet": [1, 103, 104, 109], "nois": [1, 2, 3, 39, 46, 49, 59, 64, 91, 92, 97, 98, 103, 108], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 17, 18, 19, 23, 24, 25, 27, 32, 34, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 91, 97, 100, 102, 106, 107], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 19, 35, 37, 39, 43, 45, 46, 49, 51, 52, 59, 63, 64, 65, 66, 67, 72, 73, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 97, 100, 102, 103, 106, 107, 108, 109], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 33, 34, 42, 43, 44, 45, 46, 49, 51, 55, 59, 62, 63, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 88, 91, 95, 100, 102, 103, 107], "specif": [1, 3, 5, 9, 13, 17, 18, 19, 30, 36, 37, 42, 54, 55, 56, 61, 65, 68, 71, 80, 84, 93, 95, 96, 97, 100, 101, 105, 110], "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, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "modul": [1, 3, 10, 13, 14, 16, 17, 18, 19, 24, 27, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 46, 51, 53, 54, 56, 57, 59, 61, 63, 68, 71, 72, 73, 85, 93, 99, 104], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 17, 19, 21, 26, 33, 37, 39, 40, 41, 43, 44, 46, 49, 53, 54, 56, 57, 59, 62, 63, 64, 65, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 103, 106, 107, 108, 109, 110], "gener": [1, 2, 3, 7, 10, 21, 26, 28, 36, 39, 51, 54, 56, 59, 60, 72, 73, 75, 80, 89, 90, 91, 92, 93, 96, 98, 99, 100, 101, 103, 104, 106, 107, 109, 110], "valid": [1, 2, 3, 5, 10, 15, 35, 37, 39, 46, 47, 49, 50, 51, 54, 56, 57, 59, 63, 65, 68, 71, 73, 75, 76, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "matric": [1, 3, 49, 99], "which": [1, 2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 21, 25, 29, 35, 36, 37, 39, 40, 44, 45, 46, 49, 51, 55, 56, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "learn": [1, 2, 3, 4, 5, 9, 10, 17, 19, 25, 33, 36, 41, 42, 43, 44, 46, 48, 50, 55, 56, 59, 61, 63, 65, 72, 74, 76, 79, 83, 85, 88, 89, 90, 91, 93, 95, 96, 97, 98, 100, 103, 104, 108], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 103, 104, 105, 106, 108, 109, 110], "possibl": [1, 2, 3, 7, 10, 39, 40, 44, 46, 48, 49, 51, 65, 66, 67, 68, 70, 71, 72, 73, 75, 81, 83, 84, 92, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110], "noisi": [1, 2, 3, 10, 34, 39, 41, 44, 46, 49, 59, 64, 65, 67, 73, 75, 76, 77, 79, 80, 86, 91, 92, 95, 96, 97, 99, 102, 103], "given": [1, 2, 3, 5, 10, 17, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 58, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "matrix": [1, 2, 3, 5, 10, 13, 19, 21, 34, 39, 46, 48, 49, 52, 54, 59, 60, 65, 68, 70, 71, 72, 73, 95, 97, 105, 106], "trace": [1, 91, 92, 101, 103, 104], "valu": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 21, 25, 29, 30, 35, 37, 39, 40, 41, 43, 44, 46, 48, 49, 51, 54, 55, 56, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 84, 89, 90, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "more": [1, 2, 3, 4, 5, 7, 9, 10, 13, 16, 17, 19, 21, 29, 39, 40, 43, 44, 45, 48, 51, 54, 55, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 85, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 109, 110], "function": [1, 2, 3, 4, 5, 7, 10, 13, 16, 17, 19, 26, 29, 33, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 97, 98, 99, 100, 101, 103, 104, 105, 109, 110], "noise_matrix": [1, 2, 3, 10, 49, 59, 91, 92, 101, 103, 104], "py": [1, 3, 36, 40, 41, 46, 49, 51, 91, 92, 101, 103, 104], "verbos": [1, 2, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 43, 46, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 91, 97, 101, 103], "fals": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 50, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 105, 106, 108, 109], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "prior": [1, 2, 3, 39, 46, 49, 51], "repres": [1, 2, 3, 7, 10, 13, 15, 19, 21, 29, 35, 37, 39, 43, 46, 49, 52, 54, 55, 57, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 110], "p": [1, 2, 3, 5, 10, 39, 46, 48, 49, 57, 59, 63, 71, 72, 73, 77, 95, 96, 97, 100, 101, 103, 110], "true_label": [1, 2, 3, 39, 49, 59, 101, 103], "k": [1, 2, 3, 4, 5, 8, 10, 13, 15, 19, 21, 22, 26, 29, 31, 34, 39, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 88, 90, 91, 92, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "check": [1, 2, 5, 6, 9, 10, 13, 15, 19, 30, 37, 40, 43, 44, 50, 60, 62, 68, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 104, 108], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 15, 16, 25, 29, 41, 44, 49, 51, 57, 70, 75, 89, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108], "achiev": [1, 2, 40, 41, 44, 75, 99, 100, 103, 110], "better": [1, 5, 10, 46, 55, 63, 65, 73, 75, 76, 85, 89, 90, 92, 95, 96, 97, 99, 101, 104, 105, 106, 107, 110], "than": [1, 2, 3, 4, 7, 9, 10, 29, 31, 34, 39, 46, 55, 59, 62, 63, 68, 70, 72, 73, 75, 79, 83, 88, 90, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "random": [1, 2, 3, 7, 10, 21, 34, 43, 51, 54, 63, 73, 75, 88, 90, 91, 92, 93, 95, 97, 99, 100, 101, 103, 104, 106], "perform": [1, 2, 4, 7, 10, 29, 31, 34, 40, 44, 51, 53, 54, 55, 71, 75, 85, 88, 89, 91, 99, 101, 102, 103, 104, 107, 108], "averag": [1, 3, 5, 10, 25, 31, 39, 40, 44, 51, 57, 63, 64, 71, 72, 73, 99, 103, 106], "amount": [1, 3, 93], "paramet": [1, 2, 3, 4, 5, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 92, 93, 96, 97, 100], "np": [1, 2, 3, 4, 5, 7, 13, 19, 21, 34, 39, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "ndarrai": [1, 2, 3, 4, 5, 13, 19, 26, 28, 29, 33, 34, 35, 39, 41, 43, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 97, 110], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 56, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 85, 88, 89, 91, 92, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 15, 19, 21, 29, 35, 39, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "shape": [1, 2, 3, 4, 5, 13, 19, 21, 39, 41, 43, 45, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 90, 97, 98, 99, 101, 104, 105, 106, 109, 110], "condit": [1, 2, 3, 10, 49, 55, 58, 59, 73, 93, 101, 110], "probabl": [1, 2, 3, 5, 8, 10, 13, 19, 26, 28, 31, 34, 35, 39, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 86, 98, 99, 101, 102, 104, 105, 106, 109, 110], "k_": [1, 2, 3, 49, 59], "k_y": [1, 2, 3, 49, 59], "contain": [1, 2, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 46, 48, 49, 53, 54, 58, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109], "fraction": [1, 2, 3, 10, 23, 41, 49, 59, 63, 75, 95, 99, 100], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 100, 103, 104, 105, 107, 108, 109, 110], "everi": [1, 2, 3, 4, 5, 10, 13, 19, 40, 44, 46, 49, 58, 59, 65, 73, 75, 76, 88, 90, 91, 92, 93, 95, 96, 99, 103, 105, 107, 109, 110], "class": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 56, 58, 59, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 108, 110], "other": [1, 2, 3, 5, 10, 13, 19, 25, 30, 39, 40, 42, 43, 44, 46, 49, 52, 54, 59, 60, 61, 63, 64, 67, 71, 72, 73, 75, 80, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 106, 109, 110], "assum": [1, 2, 3, 15, 46, 49, 54, 58, 59, 73, 77, 80, 97, 99, 100, 104, 106, 108, 109, 110], "column": [1, 2, 3, 5, 10, 11, 13, 15, 16, 33, 39, 43, 46, 49, 51, 52, 55, 58, 59, 63, 64, 65, 67, 68, 71, 72, 73, 75, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "sum": [1, 2, 3, 29, 34, 35, 39, 49, 51, 59, 64, 65, 67, 70, 75, 91, 92, 93, 99, 101, 103, 104, 109, 110], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 98, 99, 107], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 17, 19, 23, 25, 26, 28, 29, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 57, 59, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "true": [1, 2, 3, 5, 7, 10, 13, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 51, 54, 58, 59, 60, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "return": [1, 2, 3, 4, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 104, 105, 108, 109, 110], "bool": [1, 2, 3, 5, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 51, 54, 58, 59, 63, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 84], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 40, 43, 44, 46, 54, 59, 63, 64, 65, 67, 68, 84, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 108, 110], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 17, 19, 21, 25, 26, 30, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 63, 65, 67, 70, 71, 72, 73, 75, 76, 81, 83, 84, 85, 90, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 109, 110], "perfect": [1, 2, 39, 75, 101, 105], "exactli": [1, 3, 10, 39, 40, 44, 46, 66, 72, 91, 92, 93, 95, 96, 100, 101], "yield": [1, 40, 44, 100], "between": [1, 5, 9, 13, 14, 18, 19, 24, 25, 27, 29, 32, 35, 39, 40, 41, 42, 43, 44, 46, 47, 48, 50, 54, 55, 56, 57, 61, 63, 64, 67, 70, 72, 73, 75, 76, 79, 83, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "below": [1, 3, 4, 5, 10, 39, 40, 43, 44, 46, 48, 51, 57, 63, 64, 65, 70, 71, 79, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "we": [1, 2, 3, 5, 7, 10, 13, 16, 25, 40, 43, 44, 46, 51, 59, 60, 62, 63, 70, 71, 73, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "loop": [1, 3, 49, 59, 93, 105], "implement": [1, 2, 3, 4, 9, 17, 25, 40, 41, 43, 44, 49, 53, 55, 56, 59, 72, 75, 85, 88, 90, 91, 95, 100, 106, 107], "what": [1, 5, 9, 10, 13, 19, 36, 39, 41, 43, 46, 63, 64, 68, 70, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "doe": [1, 2, 3, 7, 10, 43, 44, 46, 51, 54, 57, 60, 70, 71, 75, 77, 79, 83, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 104, 108, 109], "do": [1, 2, 5, 9, 10, 39, 43, 44, 59, 60, 72, 73, 77, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 106, 108, 109, 110], "fast": 1, "explain": [1, 10, 97], "python": [1, 2, 44, 62, 75, 91, 92, 98, 106], "pseudocod": [1, 107], "happen": [1, 10, 46, 65, 96, 103, 109], "n": [1, 2, 3, 5, 7, 39, 40, 43, 44, 46, 48, 49, 50, 51, 54, 55, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 88, 89, 90, 93, 96, 97, 98, 99, 103, 104, 105, 108, 109, 110], "without": [1, 2, 5, 9, 10, 15, 17, 23, 40, 44, 56, 67, 75, 85, 89, 90, 96, 97, 99, 100, 101, 105, 106], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 48, 50, 57, 58, 59, 62, 63, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109], "distinct": [1, 10, 21, 59, 110], "natur": [1, 10, 103, 106], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 83, 84, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 109, 110], "0": [1, 2, 3, 4, 5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "count_joint": 1, "len": [1, 2, 3, 7, 39, 43, 49, 58, 59, 60, 72, 73, 75, 88, 89, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "y": [1, 2, 3, 5, 8, 21, 33, 34, 44, 49, 51, 59, 60, 62, 71, 75, 76, 89, 90, 91, 92, 95, 97, 99, 101, 103, 104, 106, 108], "round": [1, 43, 46, 59, 75, 97, 99, 100, 108], "astyp": [1, 100, 103], "int": [1, 2, 3, 4, 5, 7, 13, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 41, 43, 44, 46, 51, 52, 54, 55, 56, 57, 58, 59, 60, 64, 65, 67, 71, 72, 73, 75, 77, 79, 80, 81, 84, 90, 91, 93, 97, 100, 105, 106], "rang": [1, 3, 5, 7, 10, 15, 49, 51, 57, 59, 71, 75, 76, 93, 97, 98, 99, 101, 103, 104, 105, 106, 108, 109, 110], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 15, 16, 19, 25, 39, 43, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "pragma": 1, "cover": [1, 3, 86, 97, 98, 99], "choic": [1, 8, 46, 55, 57, 93, 99, 104, 106], "replac": [1, 58, 62, 73, 88, 89, 91, 92, 93, 96, 97, 98, 99, 103, 106], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 54, 73, 90, 91, 92], "05": [1, 10, 29, 33, 58, 71, 75, 81, 83, 95, 98, 99, 100, 101, 105], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 91, 92, 101, 103, 104], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 91, 92, 93, 97, 99, 100, 101, 103, 104, 109], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 29, 42, 44, 51, 75, 88, 90, 91, 92, 95, 97, 98, 100, 101, 103, 104], "max_it": [1, 89, 90, 96, 106], "10000": [1, 43, 98, 99], "x": [1, 2, 3, 5, 10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 40, 41, 44, 46, 48, 49, 51, 54, 56, 58, 59, 60, 62, 63, 65, 71, 72, 73, 75, 77, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 106, 108], "diagon": [1, 3, 5, 46, 49, 59], "equal": [1, 3, 10, 15, 54, 65, 70, 80, 107], "creat": [1, 2, 9, 13, 19, 21, 40, 43, 44, 46, 59, 75, 85, 89, 90, 93, 95, 96, 97, 99, 100, 109, 110], "impli": [1, 10, 39, 64, 71], "float": [1, 2, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 42, 43, 44, 46, 48, 50, 51, 57, 58, 59, 63, 64, 65, 67, 70, 71, 75, 79, 83, 90, 91, 92, 100, 101, 103, 104], "entri": [1, 3, 5, 10, 39, 40, 44, 46, 48, 52, 54, 57, 59, 63, 64, 65, 68, 88, 89, 95, 96, 101, 104, 105, 108], "maximum": [1, 10, 13, 72, 80, 84, 97, 109], "minimum": [1, 8, 10, 13, 23, 46, 48, 65, 70, 83, 97], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 13, 19, 29, 40, 44, 46, 54, 70, 75, 91, 99, 100, 101, 103, 105, 106], "default": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 31, 33, 36, 39, 40, 41, 43, 44, 46, 48, 49, 51, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 91, 93, 97, 99, 108, 109], "If": [1, 2, 3, 4, 5, 10, 13, 15, 16, 19, 29, 31, 37, 39, 40, 43, 44, 46, 48, 49, 51, 54, 55, 58, 59, 62, 63, 64, 65, 68, 70, 71, 72, 75, 76, 77, 79, 80, 83, 84, 85, 86, 88, 89, 90, 91, 93, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "have": [1, 2, 3, 4, 5, 7, 9, 10, 13, 19, 24, 27, 29, 32, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 76, 80, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 16, 17, 19, 25, 36, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 58, 59, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 75, 77, 79, 80, 81, 83, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "necessari": [1, 2, 3, 4, 7, 10, 15, 58, 91, 97], "In": [1, 2, 3, 5, 10, 39, 40, 43, 44, 54, 62, 63, 64, 66, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110], "particular": [1, 5, 6, 10, 13, 16, 17, 19, 22, 23, 25, 29, 30, 31, 34, 40, 44, 59, 63, 67, 71, 75, 80, 84, 85, 88, 89, 90, 92, 96, 99, 103, 104, 106, 108], "satisfi": [1, 3, 39], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 15, 33, 38, 40, 41, 42, 43, 44, 46, 49, 54, 56, 59, 61, 62, 65, 72, 73, 75, 77, 85, 86, 90, 97, 98, 99, 100, 101, 107], "argument": [1, 2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 34, 35, 40, 43, 44, 45, 46, 51, 54, 56, 60, 62, 63, 64, 65, 67, 70, 71, 72, 73, 75, 79, 80, 81, 83, 89, 92, 93, 96, 97, 98, 99, 104, 105, 108, 110], "when": [1, 2, 3, 4, 5, 10, 15, 17, 26, 29, 40, 44, 46, 49, 51, 54, 56, 57, 59, 62, 65, 67, 68, 70, 72, 73, 75, 76, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 107, 108, 109, 110], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 62, 63, 64, 65, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "rate": [1, 2, 3, 10, 41, 59, 90, 110], "set": [1, 2, 3, 5, 9, 10, 13, 15, 16, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 40, 43, 44, 46, 50, 51, 53, 54, 55, 57, 59, 62, 63, 65, 68, 70, 71, 72, 73, 75, 77, 79, 80, 88, 89, 91, 92, 95, 96, 97, 99, 100, 103, 104, 106, 107, 108, 109, 110], "note": [1, 2, 3, 7, 8, 10, 11, 15, 30, 34, 37, 40, 43, 44, 45, 46, 51, 54, 59, 62, 63, 68, 70, 71, 72, 73, 75, 76, 80, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "you": [1, 2, 3, 5, 7, 9, 10, 13, 17, 19, 39, 40, 42, 43, 44, 46, 51, 56, 61, 62, 63, 65, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110], "high": [1, 2, 10, 19, 43, 46, 54, 55, 59, 70, 73, 75, 88, 89, 91, 92, 93, 97, 98, 100, 101, 105, 108, 109, 110], "mai": [1, 2, 3, 4, 5, 10, 13, 16, 24, 25, 27, 32, 35, 39, 40, 42, 43, 44, 46, 49, 51, 54, 59, 63, 64, 68, 70, 71, 72, 73, 75, 77, 80, 84, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "imposs": [1, 10, 101], "also": [1, 2, 3, 5, 7, 9, 10, 25, 37, 39, 40, 43, 44, 46, 51, 58, 62, 63, 72, 75, 80, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "low": [1, 10, 13, 59, 63, 85, 91, 92, 96, 97, 101, 105, 109], "zero": [1, 3, 5, 40, 44, 48, 54, 59, 60, 91, 93, 104, 105, 106], "forc": [1, 2, 3, 5, 44, 91, 110], "instead": [1, 2, 3, 10, 13, 16, 19, 36, 39, 40, 43, 44, 46, 49, 59, 62, 63, 65, 67, 71, 72, 73, 75, 76, 79, 81, 83, 86, 88, 89, 90, 93, 95, 97, 99, 100, 101, 104, 105, 106, 108, 109, 110], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 13, 19, 26, 29, 33, 39, 40, 43, 44, 45, 46, 48, 49, 54, 55, 57, 58, 59, 60, 62, 63, 72, 73, 75, 77, 79, 83, 84, 85, 89, 90, 91, 92, 93, 96, 97, 100, 103, 104, 105, 106, 107, 108, 109, 110], "guarante": [1, 3, 5, 14, 18, 24, 27, 32, 40, 42, 44, 47, 49, 61, 86], "produc": [1, 2, 5, 9, 10, 13, 19, 51, 63, 73, 75, 77, 79, 85, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "higher": [1, 5, 10, 39, 46, 48, 49, 51, 57, 62, 63, 64, 75, 92, 96, 97, 99, 105], "opposit": [1, 110], "occur": [1, 3, 10, 39, 58, 70, 91, 92, 93, 99, 100, 106], "small": [1, 3, 10, 39, 43, 51, 54, 57, 59, 64, 71, 89, 93, 96, 98, 100, 104, 106], "numpi": [1, 3, 4, 5, 7, 10, 15, 21, 34, 35, 43, 44, 45, 51, 54, 57, 58, 60, 62, 67, 70, 75, 76, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "max": [1, 46, 72, 73, 92, 93, 97, 100, 106], "tri": [1, 40, 44, 107], "befor": [1, 2, 3, 10, 40, 44, 57, 59, 72, 75, 80, 88, 89, 96, 97, 99, 100, 101, 103, 106, 108], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 15, 16, 19, 26, 31, 33, 39, 40, 43, 44, 46, 49, 51, 54, 56, 57, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 80, 83, 84, 85, 88, 90, 91, 92, 93, 95, 99, 101, 104, 108, 109], "left": [1, 2, 46, 48, 57, 59, 65, 68, 71, 91, 92, 104, 105, 106, 109], "stochast": 1, "exceed": 1, "m": [1, 5, 40, 44, 50, 51, 54, 55, 63, 68, 70, 71, 72, 91, 92, 98, 103, 104, 105, 110], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 40, 44, 62, 99, 101, 109], "length": [1, 5, 15, 29, 30, 39, 41, 46, 59, 65, 68, 72, 73, 75, 77, 80, 84, 88, 90, 97, 100, 104, 106, 109, 110], "must": [1, 2, 3, 4, 5, 7, 13, 19, 39, 40, 41, 42, 44, 46, 49, 51, 52, 57, 59, 61, 62, 63, 64, 65, 72, 73, 75, 77, 79, 80, 81, 83, 84, 90, 97, 100, 103, 107, 109, 110], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 15, 39, 43, 46, 52, 59, 60, 63, 65, 71, 77, 79, 80, 81, 83, 84, 88, 89, 90, 99, 100, 103, 104, 105, 109, 110], "ball": [1, 98], "bin": [1, 3, 65, 91, 92, 106], "ensur": [1, 2, 10, 40, 44, 54, 56, 57, 59, 60, 62, 70, 73, 75, 88, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 106, 107, 108], "most": [1, 3, 5, 7, 10, 13, 19, 39, 43, 46, 51, 62, 63, 64, 65, 68, 70, 71, 72, 73, 76, 79, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109], "least": [1, 4, 10, 21, 34, 39, 43, 63, 64, 70, 73, 83, 93, 99, 100, 103, 106, 109], "int_arrai": [1, 59], "can": [2, 3, 4, 5, 7, 8, 9, 13, 16, 17, 19, 36, 37, 39, 40, 41, 42, 43, 44, 46, 50, 51, 52, 54, 55, 56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 75, 76, 77, 80, 81, 84, 85, 86, 88, 89, 90, 91, 93, 95, 96, 97, 100, 104, 105, 106, 107, 108, 109, 110], "model": [2, 3, 4, 5, 9, 10, 11, 13, 19, 21, 33, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 56, 58, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 86, 91, 92, 97, 98, 102, 107, 109, 110], "For": [2, 3, 5, 7, 9, 10, 12, 13, 19, 25, 38, 39, 40, 43, 44, 46, 49, 51, 54, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 109, 110], "regular": [2, 3, 43, 62], "multi": [2, 3, 4, 10, 35, 39, 40, 43, 44, 46, 50, 51, 52, 59, 60, 64, 65, 66, 67, 72, 73, 85, 97, 99, 100, 101, 102], "task": [2, 5, 7, 10, 11, 12, 13, 15, 17, 18, 19, 28, 33, 36, 39, 43, 49, 51, 52, 57, 59, 63, 65, 73, 75, 85, 89, 90, 96, 97, 98, 99, 100, 101, 104, 106, 108, 109, 110], "cleanlearn": [2, 3, 10, 26, 33, 40, 59, 62, 74, 75, 76, 85, 86, 88, 89, 100, 108], "wrap": [2, 40, 44, 53, 62, 72, 75, 85, 88, 89, 91, 92, 95, 96, 101, 108], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 62, 71, 72, 75, 80, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "sklearn": [2, 3, 4, 5, 8, 10, 21, 34, 39, 44, 51, 55, 56, 59, 62, 72, 75, 76, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108], "classifi": [2, 3, 44, 51, 59, 63, 66, 72, 73, 85, 86, 88, 89, 90, 95, 96, 99, 103, 104, 106, 107, 109, 110], "adher": [2, 44, 75], "estim": [2, 3, 4, 5, 9, 13, 16, 25, 39, 43, 44, 46, 49, 59, 63, 64, 65, 70, 72, 75, 77, 79, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110], "api": [2, 3, 17, 62, 68, 71, 72, 75, 86, 97, 99, 108], "defin": [2, 3, 5, 7, 10, 17, 25, 39, 40, 41, 43, 44, 46, 73, 75, 77, 85, 91, 92, 95, 98, 99, 100, 103, 106, 110], "four": [2, 10, 98, 101, 110], "clf": [2, 3, 5, 51, 75, 85, 88, 95, 97, 99, 100, 101, 104], "fit": [2, 3, 5, 8, 10, 21, 42, 44, 54, 56, 61, 62, 72, 74, 75, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 107, 108, 110], "sample_weight": [2, 44, 75, 101], "predict_proba": [2, 5, 39, 42, 44, 51, 61, 62, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 106], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 13, 19, 25, 26, 28, 31, 33, 34, 35, 37, 39, 42, 43, 44, 45, 46, 48, 49, 51, 52, 58, 59, 61, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 85, 86, 89, 98, 99, 101, 102, 106, 108, 109, 110], "score": [2, 3, 4, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 45, 46, 48, 51, 57, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 79, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 106, 108], "data": [2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 41, 42, 43, 44, 45, 46, 51, 52, 54, 55, 56, 59, 61, 62, 63, 64, 65, 66, 70, 72, 73, 74, 75, 80, 81, 82, 83, 84, 86, 93, 94, 102], "e": [2, 3, 5, 10, 15, 25, 35, 39, 40, 43, 44, 46, 49, 51, 52, 54, 59, 60, 63, 64, 65, 66, 68, 71, 72, 73, 75, 77, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "featur": [2, 3, 4, 5, 8, 10, 11, 13, 19, 21, 22, 26, 29, 30, 31, 33, 34, 51, 54, 55, 56, 59, 72, 75, 85, 88, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 108], "element": [2, 3, 5, 39, 45, 46, 48, 59, 63, 65, 73, 80, 81, 83, 89, 90, 96, 97, 99, 110], "first": [2, 5, 10, 20, 29, 30, 39, 43, 51, 54, 59, 63, 64, 68, 71, 73, 75, 85, 88, 89, 90, 91, 93, 95, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "index": [2, 10, 29, 39, 46, 53, 54, 56, 58, 59, 60, 64, 73, 75, 80, 83, 84, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "should": [2, 3, 5, 7, 10, 17, 25, 29, 34, 35, 39, 40, 43, 44, 46, 48, 49, 51, 54, 56, 57, 58, 59, 62, 63, 64, 67, 68, 70, 71, 72, 73, 75, 76, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "correspond": [2, 3, 5, 10, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 39, 40, 43, 44, 45, 46, 48, 49, 51, 54, 58, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 77, 80, 81, 83, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "differ": [2, 5, 7, 10, 13, 14, 16, 18, 24, 27, 29, 30, 32, 39, 40, 42, 43, 44, 46, 47, 51, 54, 57, 59, 60, 61, 63, 68, 70, 72, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 106, 107, 108], "sampl": [2, 3, 5, 8, 10, 13, 19, 23, 34, 46, 48, 51, 54, 55, 56, 65, 68, 71, 73, 75, 76, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 108, 109, 110], "size": [2, 10, 34, 40, 43, 44, 46, 51, 54, 55, 65, 70, 71, 75, 77, 79, 89, 93, 95, 99, 101, 103, 104, 105, 107, 109], "here": [2, 5, 7, 10, 17, 43, 46, 49, 62, 63, 64, 65, 67, 68, 71, 72, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "re": [2, 5, 40, 44, 56, 58, 63, 75, 85, 88, 89, 90, 91, 95, 96, 97, 99, 100, 108, 109, 110], "weight": [2, 10, 40, 41, 44, 51, 54, 63, 70, 73, 75, 89, 90, 91, 92, 96], "loss": [2, 41, 62, 73, 75, 93, 100], "while": [2, 3, 10, 40, 43, 44, 50, 51, 59, 75, 85, 93, 97, 99, 100, 101, 103, 104, 108], "train": [2, 3, 4, 5, 9, 10, 13, 19, 21, 35, 40, 41, 42, 44, 51, 59, 62, 63, 68, 71, 72, 75, 76, 86, 91, 92, 93, 95, 96, 98, 101, 102, 103, 104, 105, 107, 109, 110], "support": [2, 3, 4, 5, 13, 15, 17, 36, 37, 43, 45, 51, 59, 60, 62, 72, 73, 83, 85, 86, 90, 91, 92, 93, 97, 99], "your": [2, 3, 5, 9, 10, 13, 19, 39, 40, 42, 43, 44, 46, 51, 56, 59, 61, 62, 63, 64, 65, 67, 72, 73, 75, 76, 77, 79, 80, 86, 88, 89, 90, 93, 95, 98, 100, 103, 104, 105, 106, 107, 108, 109, 110], "recommend": [2, 5, 7, 10, 13, 16, 19, 43, 46, 63, 91, 92, 93, 97, 99, 100, 107, 108], "furthermor": 2, "correctli": [2, 3, 10, 39, 40, 44, 46, 49, 54, 60, 64, 65, 70, 71, 75, 77, 89, 96, 97, 99, 104, 105, 108, 109], "clonabl": [2, 75], "via": [2, 5, 7, 10, 11, 13, 16, 19, 21, 25, 39, 41, 43, 44, 51, 55, 59, 63, 68, 71, 72, 73, 75, 76, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 102, 104, 105, 106, 107, 108, 109, 110], "base": [2, 3, 4, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 45, 46, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 67, 70, 72, 73, 75, 76, 79, 81, 83, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "clone": [2, 75, 104], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 43, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 67, 71, 75, 81, 86, 91, 97, 99, 101, 103, 104, 105, 106, 108, 110], "multipl": [2, 3, 5, 10, 13, 15, 16, 37, 39, 46, 57, 58, 63, 64, 65, 67, 70, 71, 75, 85, 91, 92, 93, 95, 99, 102, 104, 105, 108], "g": [2, 3, 5, 10, 15, 25, 35, 39, 40, 44, 46, 52, 54, 59, 65, 66, 68, 71, 72, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108], "manual": [2, 75, 85, 88, 89, 90, 97, 99, 106, 107, 108, 110], "pytorch": [2, 40, 41, 44, 75, 85, 90, 93, 99, 102, 104, 109], "call": [2, 3, 5, 6, 10, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 51, 59, 62, 72, 75, 89, 90, 91, 92, 96, 99, 101, 104, 106, 107, 108, 109, 110], "__init__": [2, 41, 75, 93], "independ": [2, 3, 10, 64, 75, 96, 97, 100, 107, 108, 110], "compat": [2, 40, 43, 44, 56, 62, 75, 76, 79, 83, 85, 88, 89, 97, 99, 107, 108], "neural": [2, 41, 62, 72, 75, 90, 93, 99, 104, 106, 108], "network": [2, 40, 41, 44, 62, 72, 75, 89, 90, 93, 96, 99, 104, 106, 108], "typic": [2, 10, 40, 44, 56, 72, 75, 88, 89, 90, 92, 93, 95, 96, 100, 106, 107], "initi": [2, 3, 10, 16, 21, 40, 44, 54, 63, 75, 88, 96, 99, 100], "insid": [2, 44, 75, 99, 101], "There": [2, 3, 7, 54, 85, 101, 103], "two": [2, 3, 10, 21, 29, 39, 40, 43, 44, 52, 54, 55, 56, 59, 68, 70, 71, 86, 89, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "new": [2, 7, 9, 10, 17, 25, 40, 43, 44, 50, 54, 58, 59, 63, 75, 89, 90, 91, 96, 98, 99, 100, 106, 107, 110], "notion": 2, "confid": [2, 3, 10, 25, 39, 43, 46, 49, 51, 59, 63, 64, 65, 68, 70, 71, 72, 73, 75, 79, 83, 85, 88, 93, 100, 101, 103, 104, 105, 107, 109, 110], "packag": [2, 5, 7, 9, 10, 12, 13, 14, 18, 38, 42, 46, 47, 59, 61, 62, 68, 71, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "prune": [2, 3, 46, 65, 75, 86, 100, 105], "everyth": [2, 71, 101], "els": [2, 71, 91, 93, 97, 98, 99, 100, 103, 104, 105], "mathemat": [2, 3, 10, 49, 104], "keep": [2, 16, 17, 59, 85, 91, 97, 98, 99, 100, 109], "belong": [2, 3, 10, 39, 46, 48, 49, 54, 64, 65, 66, 67, 72, 73, 77, 81, 83, 84, 92, 93, 100, 101, 104, 106, 109, 110], "2": [2, 3, 4, 5, 7, 10, 11, 13, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 76, 80, 81, 83, 84, 98, 99, 107], "error": [2, 3, 5, 10, 40, 44, 45, 46, 48, 49, 59, 64, 65, 67, 68, 70, 71, 73, 75, 77, 79, 80, 83, 86, 88, 90, 91, 92, 95, 96, 97, 98, 100, 102], "erron": [2, 3, 39, 46, 49, 59, 64, 65, 73, 75, 76, 77, 106, 108], "import": [2, 3, 4, 5, 7, 8, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 43, 45, 51, 54, 57, 58, 63, 67, 70, 75, 76, 81, 83, 84, 85, 88, 89, 95, 96, 97, 99, 100, 104, 105, 106, 108, 109, 110], "linear_model": [2, 5, 39, 59, 75, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logisticregress": [2, 3, 5, 39, 59, 85, 89, 90, 91, 92, 96, 97, 99, 101, 103, 106], "logreg": 2, "cl": [2, 17, 33, 75, 85, 88, 89, 99, 101, 108], "pass": [2, 3, 5, 8, 10, 11, 13, 15, 16, 17, 19, 26, 33, 36, 40, 43, 44, 46, 50, 51, 54, 56, 59, 62, 63, 65, 71, 72, 73, 75, 80, 81, 85, 89, 90, 91, 92, 96, 97, 98, 99, 101, 103, 105, 106, 108], "x_train": [2, 88, 91, 92, 101, 103, 104, 108], "labels_maybe_with_error": 2, "had": [2, 3, 75, 105], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 40, 42, 43, 44, 45, 46, 54, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 89, 94, 102, 103, 106, 107, 108], "pred": [2, 46, 59, 88, 89, 100, 107, 108], "x_test": [2, 88, 91, 92, 101, 104, 108], "might": [2, 5, 10, 54, 63, 75, 80, 88, 89, 91, 92, 93, 97, 99, 105], "case": [2, 3, 10, 13, 16, 39, 51, 54, 63, 75, 88, 89, 90, 91, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108, 110], "standard": [2, 3, 5, 33, 39, 46, 62, 64, 65, 67, 73, 75, 85, 88, 91, 92, 95, 98, 100, 101, 105], "adapt": [2, 12, 13, 18, 40, 42, 59, 61, 75, 106], "skorch": [2, 75, 85, 99], "kera": [2, 61, 68, 71, 75, 85, 99, 105], "scikera": [2, 62, 75, 99], "open": [2, 43, 88, 89, 92, 95, 96, 98, 101, 104, 105, 106, 108, 110], "doesn": [2, 10, 75, 85], "t": [2, 3, 4, 7, 10, 20, 30, 31, 40, 41, 43, 44, 45, 46, 51, 57, 58, 67, 72, 73, 75, 81, 83, 84, 85, 91, 92, 93, 96, 97, 98, 100, 101, 104, 105, 108, 110], "alreadi": [2, 5, 10, 13, 19, 40, 43, 44, 49, 54, 62, 63, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 105, 106, 108], "exist": [2, 5, 10, 15, 21, 40, 43, 44, 56, 58, 62, 68, 70, 72, 75, 85, 86, 88, 89, 91, 92, 96, 103, 110], "made": [2, 5, 13, 19, 40, 44, 55, 75, 88, 89, 93, 96, 97, 99, 100, 103, 105, 107, 108], "easi": [2, 12, 49, 75, 91, 92, 98, 99, 101, 104], "inherit": [2, 7, 41, 75], "baseestim": [2, 44, 75], "yourmodel": [2, 75], "def": [2, 7, 17, 40, 44, 62, 75, 89, 90, 91, 92, 93, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "self": [2, 3, 5, 7, 10, 13, 15, 16, 17, 19, 34, 40, 41, 43, 44, 46, 51, 72, 73, 75, 88, 91, 93, 97, 98, 100, 104, 109, 110], "refer": [2, 10, 13, 19, 40, 44, 45, 64, 65, 67, 68, 70, 71, 72, 75, 79, 80, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 107, 108], "origin": [2, 5, 10, 44, 45, 46, 58, 59, 62, 64, 65, 68, 71, 72, 75, 76, 79, 81, 83, 88, 89, 91, 93, 95, 96, 97, 99, 101, 105, 106, 108, 110], "total": [2, 3, 4, 39, 43, 59, 64, 84, 93, 99, 109], "state": [2, 3, 5, 40, 41, 44, 50, 75, 101, 104, 105, 110], "art": [2, 41, 101, 104], "northcutt": [2, 3, 39, 72, 73], "et": [2, 3, 39, 41, 72, 73], "al": [2, 3, 39, 41, 72, 73], "2021": [2, 3, 39, 72, 73], "weak": [2, 71], "supervis": [2, 10, 91, 92, 99, 103], "find": [2, 5, 9, 10, 13, 16, 17, 19, 22, 23, 25, 26, 28, 29, 30, 31, 34, 35, 39, 40, 42, 43, 44, 45, 46, 50, 56, 58, 59, 61, 68, 71, 72, 73, 75, 77, 81, 83, 85, 86, 91, 98, 100, 102, 107], "uncertainti": [2, 10, 48, 72, 75, 99, 106, 108], "It": [2, 3, 5, 7, 10, 15, 16, 19, 25, 30, 33, 35, 36, 37, 40, 44, 46, 49, 51, 54, 55, 57, 63, 70, 71, 75, 85, 91, 92, 93, 97, 99, 101, 104, 107], "work": [2, 3, 7, 10, 15, 33, 39, 40, 43, 44, 46, 49, 58, 59, 60, 62, 63, 73, 75, 85, 86, 89, 91, 92, 97, 98, 100, 106, 108], "includ": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 40, 42, 43, 44, 54, 58, 59, 61, 63, 64, 67, 68, 72, 73, 75, 79, 80, 81, 83, 85, 86, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 106, 110], "deep": [2, 42, 44, 61, 62, 75, 96], "see": [2, 3, 5, 7, 10, 13, 16, 17, 36, 39, 40, 43, 44, 45, 46, 51, 56, 59, 62, 64, 65, 67, 68, 71, 72, 73, 75, 81, 83, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 108, 109, 110], "subfield": 2, "theori": [2, 101], "machin": [2, 4, 5, 9, 10, 17, 19, 36, 42, 57, 61, 75, 88, 89, 91, 92, 97, 98, 100, 103], "across": [2, 3, 5, 7, 10, 13, 16, 25, 39, 43, 51, 64, 71, 72, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 107, 108], "varieti": [2, 88, 89, 99], "like": [2, 3, 5, 6, 7, 10, 17, 35, 39, 40, 43, 44, 46, 49, 59, 62, 63, 64, 67, 68, 70, 73, 75, 76, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "pu": [2, 59], "input": [2, 3, 5, 9, 13, 19, 29, 39, 40, 43, 44, 49, 51, 54, 55, 58, 59, 60, 62, 71, 75, 85, 86, 89, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 108, 109, 110], "discret": [2, 37, 46, 49, 59, 72, 73, 77, 79, 80], "vector": [2, 3, 4, 5, 10, 13, 19, 46, 49, 51, 52, 54, 59, 72, 73, 85, 89, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105, 106, 109, 110], "would": [2, 3, 5, 10, 40, 43, 44, 46, 55, 59, 65, 75, 85, 89, 91, 93, 99, 100, 101, 106, 108, 110], "obtain": [2, 5, 8, 10, 13, 19, 46, 63, 65, 68, 71, 73, 76, 90, 92, 96, 99, 103, 105, 107, 109, 110], "been": [2, 4, 39, 46, 49, 54, 58, 59, 63, 64, 68, 70, 72, 73, 75, 90, 91, 95, 97, 99, 100, 101, 103, 104, 105, 106, 109, 110], "dure": [2, 10, 19, 54, 56, 72, 75, 88, 89, 90, 95, 96, 97, 99, 101, 104, 107, 108, 110], "denot": [2, 3, 49, 51, 59, 65, 72, 73, 83], "tild": 2, "paper": [2, 4, 10, 63, 72, 81, 83, 98, 101, 103, 106, 108, 110], "cv_n_fold": [2, 3, 75, 89], "5": [2, 3, 4, 5, 8, 10, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 44, 46, 48, 50, 51, 59, 63, 64, 67, 68, 71, 75, 76, 83, 89, 91, 96, 98, 99, 104, 105, 106, 107, 109, 110], "converge_latent_estim": [2, 3], "pulearn": [2, 59], "find_label_issues_kwarg": [2, 10, 75, 86, 99, 101], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 65, 81, 99], "clean": [2, 70, 73, 75, 76, 85, 88, 89, 91, 92, 98, 108], "even": [2, 3, 7, 9, 10, 39, 43, 48, 49, 59, 75, 90, 97, 99, 100, 101, 103, 104, 105], "messi": [2, 75, 101], "ridden": [2, 75], "autom": [2, 9, 10, 75, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "robust": [2, 49, 54, 75, 92, 97, 99, 100], "prone": [2, 75], "out": [2, 3, 5, 10, 13, 19, 31, 40, 44, 46, 51, 54, 62, 65, 66, 68, 71, 72, 73, 75, 76, 84, 85, 86, 89, 97, 98, 99, 101, 102, 104, 105, 106, 108, 109, 110], "current": [2, 3, 5, 7, 10, 11, 13, 16, 17, 25, 40, 44, 45, 46, 51, 63, 70, 75, 91, 92, 99, 100, 103, 105], "intend": [2, 13, 14, 16, 17, 18, 19, 35, 36, 37, 47, 54, 63, 79, 83, 90, 91, 92, 96, 101], "A": [2, 3, 4, 5, 7, 10, 13, 15, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 39, 40, 41, 44, 46, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 107, 110], "follow": [2, 3, 10, 17, 33, 37, 39, 40, 43, 44, 51, 53, 57, 63, 64, 68, 70, 71, 72, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "tutori": [2, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "repo": 2, "wrapper": [2, 13, 62, 88, 89, 90, 108], "around": [2, 13, 70, 91, 92, 100, 105, 106, 110], "fasttext": 2, "store": [2, 4, 5, 10, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 72, 75, 88, 89, 95, 96, 97, 98, 99, 109, 110], "along": [2, 51, 65, 83, 91, 92, 93, 97, 99, 106], "dimens": [2, 59, 77, 80, 93, 99, 106, 109], "select": [2, 9, 10, 29, 53, 63, 73, 93, 100, 103, 106], "split": [2, 3, 5, 10, 15, 43, 51, 58, 59, 75, 88, 90, 91, 92, 93, 95, 96, 97, 98, 101, 102, 104, 107, 110], "cross": [2, 3, 10, 39, 46, 49, 50, 51, 65, 68, 71, 73, 75, 76, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 104, 105, 108, 109, 110], "fold": [2, 3, 39, 46, 49, 75, 88, 90, 95, 98, 99, 105, 109], "By": [2, 39, 64, 65, 75, 91, 97, 109], "need": [2, 3, 10, 11, 39, 40, 43, 44, 46, 54, 56, 64, 65, 67, 72, 75, 85, 89, 90, 91, 92, 96, 97, 99, 100, 101, 103, 104, 105, 109], "holdout": [2, 3, 75], "comput": [2, 3, 4, 5, 7, 8, 10, 13, 22, 23, 25, 26, 29, 30, 31, 34, 39, 40, 41, 43, 44, 46, 48, 49, 50, 51, 54, 55, 56, 59, 63, 64, 65, 67, 70, 71, 72, 73, 75, 76, 77, 79, 85, 86, 89, 91, 92, 98, 101, 102, 105, 106, 108, 109], "them": [2, 3, 5, 7, 9, 10, 12, 15, 30, 35, 38, 40, 42, 43, 44, 46, 56, 61, 63, 72, 75, 86, 88, 89, 91, 92, 93, 95, 96, 97, 99, 103, 104, 106, 108, 109, 110], "numer": [2, 3, 4, 5, 10, 13, 16, 25, 33, 37, 51, 54, 55, 70, 72, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 100, 101, 103, 104, 106, 108], "consist": [2, 3, 10, 40, 44, 53, 59, 63, 97, 109, 110], "latent": [2, 3, 49], "thei": [2, 3, 5, 10, 14, 18, 24, 27, 29, 32, 40, 41, 42, 44, 46, 47, 54, 57, 59, 62, 65, 70, 73, 75, 76, 79, 83, 85, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108, 110], "relat": [2, 3, 10, 16, 22, 23, 29, 30, 31, 34, 49, 59, 64, 75, 92, 96, 97], "close": [2, 3, 10, 43, 49, 72, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105], "form": [2, 3, 10, 40, 41, 44, 49, 58, 59, 73, 75, 99], "equival": [2, 3, 40, 44, 49, 72, 106, 108], "iter": [2, 3, 39, 40, 44, 46, 59, 64, 65, 75, 99, 103, 109], "enforc": [2, 40, 44, 59], "perfectli": [2, 39, 64, 101], "certain": [2, 3, 5, 10, 40, 44, 62, 71, 75, 91, 92, 97, 98, 105, 106], "dict": [2, 3, 5, 10, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 43, 44, 46, 50, 51, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 83, 91, 92, 93, 99, 100, 110], "keyword": [2, 3, 5, 10, 11, 13, 19, 26, 30, 33, 40, 43, 44, 46, 48, 51, 54, 56, 58, 62, 63, 65, 71, 72, 73, 75, 80, 81, 83, 91], "filter": [2, 3, 10, 43, 45, 58, 64, 66, 67, 69, 71, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 93, 96, 98, 99, 100, 104, 105, 108, 109, 110], "find_label_issu": [2, 3, 10, 33, 42, 43, 45, 46, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 99, 104, 105, 108, 109, 110], "particularli": [2, 85, 100, 103, 106], "filter_bi": [2, 3, 43, 46, 65, 86, 99], "frac_nois": [2, 46, 65, 81, 99], "min_examples_per_class": [2, 46, 65, 99, 101], "impact": [2, 4, 10, 91, 92, 93, 97], "ml": [2, 4, 5, 9, 10, 18, 75, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 102, 103, 104, 106, 107, 108], "accuraci": [2, 10, 41, 73, 88, 89, 90, 93, 99, 100, 101, 103, 106, 108, 109], "n_job": [2, 43, 46, 65, 77, 79, 81, 99, 100, 106, 109], "disabl": [2, 40, 44, 46, 106], "process": [2, 3, 7, 13, 16, 19, 35, 40, 43, 44, 46, 54, 58, 63, 65, 71, 77, 79, 81, 89, 90, 91, 97, 99, 100, 103, 107], "caus": [2, 46, 51, 91, 92, 97, 99], "rank": [2, 3, 10, 39, 43, 45, 46, 51, 64, 65, 66, 68, 69, 71, 72, 74, 78, 80, 81, 82, 84, 85, 86, 88, 89, 91, 92, 98, 99, 104, 105, 106, 109, 110], "get_label_quality_scor": [2, 42, 43, 45, 46, 47, 51, 63, 65, 66, 67, 68, 69, 70, 73, 74, 76, 78, 79, 81, 82, 83, 86, 99, 101, 104, 105, 109, 110], "adjust_pred_prob": [2, 10, 67, 72, 73, 101], "control": [2, 5, 9, 10, 13, 19, 43, 46, 63, 71, 72, 75, 81, 83, 91, 92, 97, 98, 99], "how": [2, 3, 5, 10, 13, 15, 16, 17, 19, 25, 39, 40, 41, 43, 44, 49, 59, 63, 64, 67, 68, 70, 72, 73, 75, 79, 83, 85, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 105, 106, 107, 108, 109], "much": [2, 10, 39, 43, 46, 75, 97, 99, 103], "output": [2, 3, 5, 10, 13, 19, 35, 40, 41, 44, 49, 59, 62, 63, 64, 68, 70, 71, 72, 75, 79, 80, 83, 84, 85, 86, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 106, 107, 108], "print": [2, 5, 7, 13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 59, 63, 64, 65, 70, 72, 73, 75, 77, 79, 80, 84, 86, 88, 89, 90, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "suppress": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80, 109, 110], "statement": [2, 43, 63, 70, 72, 73, 75, 77, 79, 80], "big": [2, 43, 65, 71, 75, 101], "limit": [2, 5, 13, 19, 43, 54, 65, 85, 97, 105, 109, 110], "memori": [2, 40, 43, 44, 65, 71, 77, 79, 91, 109], "experiment": [2, 40, 41, 43, 44, 45, 65, 86, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "label_issues_batch": [2, 42, 65, 99], "find_label_issues_batch": [2, 42, 43, 65, 99], "pred_prob": [2, 3, 5, 8, 10, 11, 13, 19, 26, 28, 29, 31, 34, 35, 39, 43, 45, 46, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 67, 68, 71, 72, 73, 77, 79, 80, 81, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108], "threshold": [2, 3, 4, 7, 10, 13, 21, 22, 23, 25, 31, 33, 34, 43, 57, 70, 71, 72, 73, 79, 83, 91, 97, 105, 106, 109, 110], "inverse_noise_matrix": [2, 3, 10, 49, 59, 86, 101], "label_issu": [2, 43, 46, 65, 68, 75, 77, 86, 88, 89, 90, 93, 96, 99, 100, 101, 104, 108], "clf_kwarg": [2, 3, 10, 75], "clf_final_kwarg": [2, 75], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 39, 43, 46, 48, 54, 63, 64, 65, 67, 68, 70, 71, 73, 75, 76, 79, 83, 85, 88, 89, 90, 92, 93, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108], "result": [2, 3, 9, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 43, 44, 46, 48, 57, 59, 65, 67, 68, 71, 73, 75, 76, 77, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 108, 109, 110], "identifi": [2, 3, 5, 7, 9, 10, 13, 15, 19, 30, 36, 39, 43, 45, 46, 54, 65, 68, 71, 73, 75, 76, 77, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 101, 104, 106, 108, 109, 110], "final": [2, 10, 75, 88, 95, 97, 100, 105, 107, 108], "remain": [2, 75, 86, 88, 89, 93, 97, 100, 104, 108, 110], "datasetlik": [2, 59, 75], "beyond": [2, 5, 7, 9, 10, 12, 38, 85, 88, 89, 100, 108, 109], "pd": [2, 3, 5, 7, 13, 16, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 50, 62, 63, 64, 75, 83, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 108, 110], "datafram": [2, 3, 5, 7, 13, 15, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 43, 50, 59, 60, 62, 63, 64, 75, 80, 84, 86, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 108, 109, 110], "scipi": [2, 4, 5, 13, 16, 55, 59, 72, 97], "spars": [2, 4, 5, 10, 13, 16, 19, 21, 34, 54, 59, 60, 95, 97], "csr_matrix": [2, 4, 5, 13, 16, 19, 21, 34, 54, 97], "torch": [2, 40, 41, 44, 89, 90, 93, 96, 98, 106], "util": [2, 5, 10, 13, 19, 36, 40, 41, 44, 47, 54, 62, 63, 68, 71, 75, 85, 86, 90, 91, 92, 93, 99, 101, 106], "tensorflow": [2, 59, 62, 85, 90, 99], "object": [2, 5, 10, 13, 15, 16, 19, 35, 36, 40, 41, 43, 44, 51, 54, 56, 59, 60, 62, 65, 68, 69, 70, 71, 72, 75, 83, 85, 89, 90, 92, 93, 95, 97, 99, 100, 101, 102, 104, 108], "list": [2, 3, 5, 10, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 41, 43, 44, 45, 46, 52, 54, 58, 59, 60, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 79, 80, 81, 83, 84, 86, 89, 90, 91, 92, 93, 98, 99, 100, 101, 104, 105, 108, 110], "index_list": 2, "subset": [2, 3, 5, 13, 19, 39, 43, 46, 59, 73, 80, 84, 88, 89, 90, 93, 95, 96, 97, 99, 104, 105, 106, 107, 108, 110], "wa": [2, 3, 15, 17, 43, 57, 59, 63, 64, 70, 72, 84, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 107, 109, 110], "abl": [2, 3, 10, 75, 90, 99, 100, 101, 103, 104], "format": [2, 3, 5, 10, 15, 35, 40, 43, 44, 46, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 65, 68, 71, 72, 73, 75, 77, 79, 80, 83, 84, 88, 91, 92, 93, 95, 97, 98, 100, 103, 108, 109, 110], "make": [2, 3, 5, 21, 40, 43, 44, 51, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 108], "sure": [2, 5, 43, 46, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 108], "shuffl": [2, 10, 59, 90, 93, 96, 97, 104, 106], "ha": [2, 3, 5, 6, 10, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 45, 49, 51, 54, 58, 59, 63, 68, 70, 75, 81, 83, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 104, 105, 106, 107, 108, 110], "batch": [2, 43, 59, 62, 63, 77, 79, 93, 99, 106], "order": [2, 5, 10, 37, 39, 40, 44, 45, 46, 49, 50, 51, 57, 59, 63, 64, 65, 68, 71, 72, 73, 77, 80, 81, 83, 84, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 108, 109, 110], "destroi": [2, 59], "oper": [2, 40, 43, 44, 54, 59, 62, 73, 85, 88, 89, 96, 99, 106], "eg": [2, 5, 10, 59, 68, 71, 91, 92, 99, 100], "repeat": [2, 59, 63, 103, 106], "appli": [2, 10, 37, 40, 42, 44, 46, 51, 52, 54, 58, 59, 67, 72, 81, 85, 88, 89, 90, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 109], "array_lik": [2, 3, 39, 46, 59, 65, 72, 76], "some": [2, 3, 5, 10, 17, 25, 39, 40, 42, 44, 46, 49, 54, 58, 59, 61, 63, 64, 65, 67, 68, 71, 72, 73, 75, 77, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "seri": [2, 3, 43, 59, 60, 75, 83, 99, 100], "row": [2, 3, 5, 10, 13, 16, 30, 35, 39, 43, 46, 48, 49, 54, 55, 59, 63, 64, 65, 67, 72, 73, 75, 80, 81, 83, 84, 88, 90, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 110], "rather": [2, 3, 5, 10, 29, 39, 59, 62, 63, 70, 79, 83, 89, 98, 100, 103, 107, 108, 109, 110], "leav": [2, 46], "per": [2, 3, 5, 7, 10, 13, 16, 39, 43, 46, 51, 58, 63, 64, 65, 67, 70, 71, 73, 76, 77, 79, 83, 92, 99, 105, 110], "determin": [2, 3, 10, 15, 19, 25, 29, 33, 39, 43, 46, 51, 54, 59, 63, 65, 68, 70, 73, 79, 83, 91, 97, 99, 100, 103, 105, 106, 108], "cutoff": [2, 3, 55, 106], "consid": [2, 3, 4, 5, 10, 13, 16, 19, 26, 29, 31, 34, 39, 40, 44, 46, 54, 56, 59, 63, 70, 72, 73, 76, 79, 83, 88, 89, 90, 93, 95, 96, 97, 99, 100, 101, 105, 106, 107, 108, 109], "section": [2, 3, 7, 10, 86, 93, 95, 97, 99, 100, 105], "3": [2, 3, 4, 5, 7, 10, 11, 37, 39, 40, 44, 46, 49, 50, 51, 52, 55, 57, 58, 59, 62, 65, 72, 73, 75, 76, 81, 83, 98, 99, 107], "equat": [2, 3, 49], "advanc": [2, 3, 5, 9, 10, 13, 19, 70, 72, 83, 86, 92, 94, 97, 99, 100, 101], "user": [2, 3, 5, 9, 10, 13, 17, 19, 30, 35, 36, 37, 40, 44, 46, 54, 62, 70, 72, 73, 75, 79, 83, 100, 101], "specifi": [2, 3, 4, 5, 8, 10, 13, 16, 17, 19, 21, 34, 36, 40, 43, 44, 46, 51, 54, 56, 58, 62, 63, 64, 65, 68, 70, 72, 73, 75, 76, 84, 86, 89, 90, 92, 93, 96, 97, 100, 103, 105, 108], "automat": [2, 3, 5, 29, 39, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "greater": [2, 3, 4, 5, 7, 9, 10, 31, 43, 55, 59, 70, 92, 98, 99, 110], "count": [2, 25, 29, 39, 43, 46, 49, 59, 64, 65, 71, 86, 93, 97, 99, 105], "observ": [2, 3, 49, 56, 90, 91, 92, 103, 106, 108], "mislabel": [2, 10, 39, 43, 45, 46, 49, 63, 64, 65, 68, 70, 73, 79, 81, 83, 84, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 105, 108], "one": [2, 3, 5, 7, 10, 29, 39, 40, 43, 44, 45, 46, 51, 57, 59, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 83, 84, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 103, 106, 107, 108, 110], "get_label_issu": [2, 42, 43, 74, 75, 88, 89, 101, 108], "either": [2, 3, 4, 7, 10, 40, 43, 44, 46, 55, 63, 65, 70, 72, 73, 77, 79, 92, 97, 99, 104, 105], "boolean": [2, 7, 10, 25, 43, 46, 56, 58, 63, 65, 68, 73, 75, 77, 79, 80, 85, 89, 90, 92, 93, 96, 99, 105, 108, 109], "label_issues_mask": [2, 46, 73, 75, 86], "indic": [2, 3, 4, 5, 7, 10, 13, 16, 25, 39, 43, 44, 45, 46, 48, 51, 54, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 75, 76, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "its": [2, 5, 7, 9, 10, 13, 19, 40, 43, 44, 46, 54, 56, 57, 58, 65, 68, 71, 72, 73, 75, 77, 81, 83, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 107, 108, 109, 110], "return_indices_ranked_bi": [2, 43, 46, 65, 81, 86, 88, 89, 99, 101], "significantli": [2, 10, 93, 97, 101, 103, 107], "reduc": [2, 43, 46, 59, 90, 99], "time": [2, 10, 40, 43, 44, 59, 63, 84, 86, 91, 93, 99, 100, 105, 109, 110], "take": [2, 5, 10, 39, 40, 44, 50, 51, 54, 56, 59, 62, 73, 88, 93, 95, 103, 104, 105, 110], "run": [2, 5, 6, 7, 9, 10, 11, 12, 13, 17, 19, 29, 30, 35, 38, 40, 43, 44, 56, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 110], "skip": [2, 10, 40, 44, 75, 90, 97, 99, 100, 104, 110], "slow": [2, 3], "step": [2, 7, 29, 51, 71, 93, 97, 100, 101, 103, 107], "caution": [2, 5, 99, 100], "previous": [2, 5, 13, 16, 59, 72, 75, 86, 88, 90, 91, 95, 96, 100, 103, 107], "assign": [2, 7, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 40, 44, 50, 51, 59, 75, 88, 91, 93, 95, 97, 99, 108, 109, 110], "individu": [2, 4, 7, 10, 13, 16, 29, 40, 44, 45, 63, 67, 70, 73, 75, 81, 83, 86, 88, 92, 95, 97, 98, 99, 103, 104, 105, 110], "still": [2, 43, 44, 59, 72, 88, 93, 99, 106], "extra": [2, 40, 44, 59, 62, 63, 64, 75, 93, 96, 99, 100, 103, 106], "receiv": [2, 10, 40, 44, 45, 64, 67, 68, 75, 77, 81, 92, 105], "overwritten": [2, 75], "callabl": [2, 3, 4, 10, 29, 40, 44, 51, 54, 55, 56, 58, 62, 67, 99], "x_val": 2, "y_val": 2, "map": [2, 3, 15, 43, 44, 47, 50, 58, 59, 71, 73, 75, 80, 90, 91, 92, 93, 97, 99, 101, 104, 110], "appropri": [2, 10, 19, 37, 55, 65, 73, 91, 95, 100, 104, 105], "earli": [2, 93], "stop": [2, 93], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 25, 39, 59, 72, 88, 91, 93, 95, 97, 100, 104, 108, 110], "f": [2, 7, 88, 89, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108], "ignor": [2, 40, 44, 58, 62, 75, 80, 84, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "allow": [2, 13, 39, 40, 43, 44, 48, 56, 59, 63, 71, 72, 75, 77, 79, 89, 90, 93, 97, 99, 107, 109], "access": [2, 10, 16, 40, 44, 75, 92, 93, 98, 104], "hyperparamet": [2, 67, 72, 93], "purpos": [2, 54, 91, 92, 97, 99, 104, 108], "want": [2, 5, 10, 39, 43, 54, 60, 63, 65, 75, 89, 91, 93, 96, 98, 100, 103, 105, 106, 107, 109, 110], "explicitli": [2, 8, 10, 44, 54, 75], "yourself": [2, 5, 43, 92, 97], "altern": [2, 7, 10, 51, 56, 59, 62, 63, 73, 86, 89, 90, 93, 95, 96, 98, 99, 100, 101, 103, 104, 106, 108], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 19, 29, 33, 40, 43, 44, 46, 54, 59, 62, 63, 65, 72, 73, 75, 79, 80, 83, 84, 85, 88, 89, 91, 92, 93, 95, 96, 97, 99, 100, 104, 105, 106, 107, 108, 109], "effect": [2, 10, 30, 40, 44, 63, 72, 75, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 108], "offer": [2, 5, 9, 10, 89, 90, 91, 92, 96, 99, 100, 101, 104], "after": [2, 3, 5, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 63, 75, 89, 91, 93, 96, 97, 99, 100, 101, 103, 105, 106, 107, 108, 109], "attribut": [2, 5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 40, 43, 44, 51, 56, 72, 75, 88, 91, 97], "label_issues_df": [2, 75, 93], "similar": [2, 10, 39, 40, 44, 56, 59, 63, 67, 68, 70, 72, 75, 79, 83, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 106, 109], "document": [2, 3, 5, 13, 17, 19, 39, 40, 43, 44, 45, 46, 51, 58, 62, 64, 65, 67, 70, 71, 72, 75, 79, 80, 81, 83, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "descript": [2, 5, 7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 39, 45, 59, 68, 75, 91, 92], "were": [2, 3, 5, 10, 39, 44, 54, 64, 70, 83, 88, 90, 95, 99, 101, 103, 105, 107, 109], "present": [2, 3, 5, 10, 13, 15, 16, 23, 39, 59, 72, 80, 85, 93, 97, 99, 100, 106], "actual": [2, 3, 5, 10, 39, 54, 63, 64, 73, 92, 99, 101, 107, 110], "num_class": [2, 39, 43, 59, 62, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 106], "uniqu": [2, 34, 59, 80, 91, 97, 99, 100, 104, 106], "given_label": [2, 5, 11, 28, 33, 39, 49, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109, 110], "normal": [2, 3, 10, 21, 29, 34, 46, 48, 51, 57, 58, 59, 73, 97, 99, 101, 106], "trick": [2, 99], "distribut": [2, 3, 5, 10, 29, 31, 39, 44, 46, 50, 57, 63, 71, 72, 73, 85, 91, 92, 93, 95, 96, 97, 100, 105, 106], "account": [2, 39, 63, 67, 72, 73, 89, 96, 99, 101, 103, 104, 106, 108], "word": [2, 3, 58, 83, 84, 99], "remov": [2, 10, 34, 39, 40, 44, 46, 75, 85, 88, 89, 93, 96, 97, 98, 99, 100, 104, 106, 108], "so": [2, 3, 5, 6, 7, 10, 17, 29, 37, 39, 40, 43, 44, 46, 54, 59, 63, 64, 70, 73, 75, 79, 83, 90, 91, 92, 93, 96, 97, 100, 101, 104, 106, 109], "proportion": [2, 10, 46], "just": [2, 3, 5, 10, 13, 16, 35, 39, 41, 43, 59, 62, 73, 75, 77, 85, 86, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 104, 105, 106, 107, 108, 109], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 16, 34, 40, 41, 44, 46, 51, 57, 58, 59, 63, 65, 67, 72, 73, 75, 76, 77, 85, 88, 89, 90, 93, 96, 97, 98, 99, 100, 101, 106, 107, 108], "detect": [2, 5, 7, 9, 13, 16, 17, 19, 21, 25, 31, 45, 54, 57, 66, 68, 69, 70, 71, 72, 73, 74, 75, 78, 82, 85, 88, 89, 91, 94, 98, 100, 102, 104, 108, 109, 110], "arg": [2, 15, 25, 30, 34, 40, 41, 44, 51, 59, 73, 75, 100], "kwarg": [2, 7, 10, 13, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 45, 51, 54, 62, 71, 75, 77, 79, 80, 81, 99], "test": [2, 5, 10, 29, 44, 51, 54, 62, 75, 85, 88, 89, 91, 92, 93, 95, 96, 102, 107, 108, 110], "expect": [2, 3, 10, 40, 44, 46, 51, 54, 63, 72, 73, 75, 88, 89, 99, 100, 101, 103, 104, 105, 108, 110], "class_predict": 2, "evalu": [2, 10, 40, 41, 42, 43, 44, 71, 75, 88, 89, 90, 91, 92, 93, 99, 101, 103, 107, 108, 109], "simpli": [2, 10, 39, 73, 85, 89, 91, 92, 95, 96, 99, 101, 104, 108, 109, 110], "quantifi": [2, 4, 5, 7, 10, 13, 16, 46, 67, 72, 75, 85, 92, 93, 95, 96, 97, 100, 101, 105], "save_spac": [2, 10, 74, 75], "potenti": [2, 10, 39, 46, 58, 65, 68, 71, 73, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "cach": [2, 89, 96], "panda": [2, 5, 7, 15, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 59, 60, 62, 63, 64, 86, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 108, 109], "unlik": [2, 10, 46, 48, 51, 62, 64, 65, 67, 83, 91, 100, 103, 104, 106, 108], "both": [2, 5, 10, 13, 19, 29, 39, 40, 44, 46, 54, 59, 63, 65, 73, 77, 79, 84, 85, 91, 93, 99, 100, 101, 103, 110], "mask": [2, 43, 46, 58, 59, 65, 68, 73, 75, 77, 79, 80, 85, 98, 99, 103, 105, 109, 110], "prefer": [2, 73, 81, 104], "plan": 2, "subsequ": [2, 3, 40, 44, 56, 89, 96, 99, 101, 105], "invok": [2, 40, 44, 101, 107], "scratch": [2, 54, 75], "To": [2, 5, 7, 9, 10, 12, 13, 16, 19, 29, 38, 40, 43, 44, 45, 46, 62, 63, 65, 67, 71, 72, 73, 75, 76, 77, 79, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 107, 108, 109, 110], "share": [2, 10, 73, 75], "mostli": [2, 59, 70, 75, 100, 104, 108], "longer": [2, 37, 50, 51, 58, 75, 86, 89, 96, 99, 100, 105], "info": [2, 5, 7, 10, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 75, 83, 92, 97, 98, 110], "about": [2, 3, 5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 41, 43, 48, 63, 64, 67, 71, 75, 80, 83, 90, 91, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106], "docstr": [2, 39, 40, 44, 59, 75, 98, 101], "unless": [2, 40, 44, 54, 75, 99], "our": [2, 3, 10, 62, 63, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "is_label_issu": [2, 11, 33, 75, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "entir": [2, 10, 29, 43, 46, 49, 64, 65, 70, 73, 75, 77, 79, 80, 85, 91, 92, 97, 99, 100, 105, 106, 107, 109, 110], "accur": [2, 3, 5, 9, 10, 13, 19, 39, 43, 46, 55, 63, 64, 65, 68, 71, 73, 75, 76, 77, 79, 80, 86, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 103, 104, 106, 108], "label_qu": [2, 63, 75, 89, 101, 103, 108], "measur": [2, 5, 39, 63, 64, 75, 85, 88, 97, 98, 99, 100, 101, 103, 104, 108, 109, 110], "qualiti": [2, 3, 5, 7, 9, 10, 13, 16, 33, 34, 39, 43, 45, 46, 48, 51, 63, 64, 65, 67, 68, 70, 73, 75, 76, 79, 81, 83, 85, 86, 90, 91, 93, 99, 100, 102], "lower": [2, 4, 5, 7, 10, 13, 16, 31, 43, 51, 57, 63, 64, 67, 70, 71, 73, 75, 76, 79, 83, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "eas": 2, "comparison": [2, 40, 44, 71, 100, 101, 103], "against": [2, 40, 44, 91, 95, 97, 99, 100, 103, 104], "predicted_label": [2, 5, 11, 28, 33, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108, 109], "ad": [2, 40, 44, 92, 103, 108], "precis": [2, 55, 57, 65, 68, 71, 97, 98, 99, 101, 109, 110], "definit": [2, 7, 37, 51, 75, 88, 95], "accessor": [2, 75], "describ": [2, 10, 21, 63, 72, 73, 75, 81, 83, 101, 103, 104, 105, 107, 110], "precomput": [2, 4, 5, 49, 54, 75, 98], "clear": [2, 40, 44, 56, 75, 89, 96, 97, 108], "save": [2, 5, 13, 19, 40, 43, 44, 71, 75, 97, 99, 105, 109, 110], "space": [2, 5, 10, 72, 75, 93, 95, 97, 98], "place": [2, 40, 44, 54, 59, 75, 88, 103], "larg": [2, 9, 10, 43, 54, 75, 93, 99, 105, 106, 109, 110], "deploi": [2, 9, 10, 75, 93, 99, 100], "care": [2, 10, 40, 44, 54, 75, 96, 97, 99, 101], "avail": [2, 4, 5, 7, 10, 15, 17, 36, 44, 56, 75, 99, 100, 101, 103, 105, 108], "cannot": [2, 5, 15, 17, 59, 100, 107, 110], "anymor": 2, "classmethod": [2, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 37, 44, 51, 75], "__init_subclass__": [2, 42, 44, 74, 75], "set_": [2, 44, 75], "_request": [2, 44, 75], "pep": [2, 44, 75], "487": [2, 44, 75], "look": [2, 5, 7, 10, 19, 40, 44, 59, 75, 80, 88, 91, 92, 95, 96, 99, 100, 101, 103, 104, 105, 106, 109, 110], "inform": [2, 5, 7, 10, 13, 16, 19, 36, 40, 44, 56, 59, 63, 64, 68, 71, 75, 80, 83, 84, 85, 90, 91, 95, 96, 97, 98, 100, 101, 103, 106, 109, 110], "__metadata_request__": [2, 44, 75], "infer": [2, 44, 59, 75, 80, 84, 88, 89, 93, 103, 104], "signatur": [2, 40, 44, 75], "accept": [2, 40, 44, 56, 57, 73, 75, 91, 92, 99], "metadata": [2, 10, 44, 75, 93, 110], "through": [2, 5, 7, 44, 75, 89, 90, 92, 96, 97, 98, 99, 100, 103, 105, 106], "develop": [2, 9, 44, 56, 75, 99, 101, 110], "request": [2, 44, 75, 88, 89, 92, 96, 97, 98, 104, 110], "those": [2, 3, 4, 10, 43, 44, 46, 53, 62, 63, 65, 71, 75, 79, 83, 84, 85, 90, 93, 97, 99, 100, 105, 109], "http": [2, 4, 5, 7, 9, 10, 12, 21, 38, 40, 41, 43, 44, 48, 56, 59, 68, 71, 72, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "www": [2, 44, 75, 106], "org": [2, 4, 21, 40, 41, 44, 56, 59, 72, 75, 99, 100, 101, 110], "dev": [2, 44, 75], "0487": [2, 44, 75], "get_metadata_rout": [2, 42, 44, 74, 75], "rout": [2, 44, 75], "pleas": [2, 40, 44, 62, 75, 85, 89, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 110], "guid": [2, 7, 10, 44, 75, 86, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101], "mechan": [2, 40, 44, 75], "metadatarequest": [2, 44, 75], "encapsul": [2, 19, 44, 70, 75], "get_param": [2, 42, 44, 61, 62, 74, 75], "subobject": [2, 44, 75], "param": [2, 10, 40, 44, 62, 72, 75, 99], "name": [2, 5, 6, 7, 10, 11, 13, 15, 16, 35, 37, 39, 40, 44, 50, 51, 55, 59, 62, 63, 64, 71, 75, 80, 84, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108, 109, 110], "set_fit_request": [2, 42, 44, 74, 75], "str": [2, 3, 4, 5, 13, 15, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 43, 44, 46, 49, 51, 54, 55, 56, 57, 58, 59, 62, 63, 64, 68, 70, 71, 73, 75, 80, 84, 90, 91, 97, 99, 103, 104, 105, 110], "unchang": [2, 40, 44, 75, 97, 110], "relev": [2, 10, 19, 29, 44, 75, 93, 95, 97], "enable_metadata_rout": [2, 44, 75], "set_config": [2, 44, 75], "meta": [2, 44, 75], "rais": [2, 4, 5, 13, 15, 16, 37, 40, 44, 48, 51, 54, 57, 75, 99], "alia": [2, 40, 44, 75], "metadata_rout": [2, 44, 75], "retain": [2, 44, 59, 75], "chang": [2, 35, 37, 40, 43, 44, 48, 75, 83, 88, 89, 90, 91, 96, 99, 100, 105, 106, 110], "version": [2, 4, 5, 7, 9, 10, 12, 14, 18, 24, 27, 32, 38, 40, 42, 44, 47, 48, 59, 61, 62, 73, 75, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108, 110], "sub": [2, 44, 70, 75], "pipelin": [2, 44, 75, 108], "otherwis": [2, 4, 7, 10, 37, 39, 40, 43, 44, 46, 52, 55, 57, 58, 59, 65, 75, 77, 79, 80, 84, 85, 89, 96, 99, 100], "updat": [2, 13, 16, 40, 43, 44, 54, 62, 75, 86, 91, 93, 100], "set_param": [2, 42, 44, 61, 62, 74, 75], "simpl": [2, 40, 44, 46, 63, 73, 75, 88, 89, 91, 92, 93, 95, 96, 100, 103, 106, 108], "well": [2, 3, 9, 10, 40, 44, 48, 49, 63, 65, 71, 73, 75, 80, 83, 84, 86, 91, 92, 93, 95, 96, 99, 100, 101, 103, 105, 106], "nest": [2, 40, 44, 45, 60, 75, 81, 83, 84, 110], "latter": [2, 40, 44, 75, 106], "compon": [2, 44, 75], "__": [2, 44, 75], "set_score_request": [2, 74, 75], "structur": [3, 72, 95, 97, 99, 100], "unobserv": 3, "less": [3, 4, 5, 10, 34, 43, 51, 63, 72, 73, 77, 79, 83, 93, 95, 97, 98, 99, 100, 101, 105, 110], "channel": [3, 90, 101], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 39, 49, 59, 64, 89, 92, 98], "inv": 3, "confident_joint": [3, 25, 39, 46, 59, 64, 65, 86, 99, 101], "un": 3, "under": [3, 10, 40, 44, 64, 71, 72, 92, 97, 100, 106], "joint": [3, 39, 46, 49, 59, 64, 65, 98], "num_label_issu": [3, 43, 46, 65, 80, 84, 86], "estimation_method": [3, 43], "off_diagon": 3, "multi_label": [3, 39, 46, 59, 60, 65, 104], "don": [3, 10, 85, 92, 93, 96, 101, 105, 108], "statis": 3, "compute_confident_joint": [3, 39, 46, 59, 65, 101], "off": [3, 46, 59, 70, 93, 101, 105, 106], "j": [3, 5, 39, 40, 44, 45, 46, 65, 68, 71, 72, 81, 83, 84, 91, 92, 101, 109, 110], "confident_learn": [3, 46, 65, 101], "off_diagonal_calibr": 3, "calibr": [3, 4, 46, 59, 63, 103], "cj": [3, 49, 59], "axi": [3, 34, 49, 51, 57, 77, 80, 90, 91, 92, 93, 97, 99, 100, 101, 103, 104, 106, 108, 109], "bincount": [3, 91, 92, 101, 103, 104], "alwai": [3, 10, 40, 44, 59, 88, 89, 90, 101, 108], "estimate_issu": 3, "over": [3, 5, 10, 40, 43, 44, 70, 71, 77, 79, 88, 92, 93, 95, 97, 98, 99, 100, 101, 106, 108], "As": [3, 7, 85, 91, 92, 96, 100, 101, 108, 110], "add": [3, 5, 7, 13, 15, 16, 40, 44, 62, 71, 89, 90, 91, 92, 93, 96, 97, 99, 100, 101, 104], "approach": [3, 39, 43, 46, 62, 88, 95, 97, 100, 101, 104, 106, 108], "custom": [3, 7, 10, 12, 33, 40, 43, 44, 51, 58, 73, 89, 92, 96, 97, 101, 108], "know": [3, 10, 91, 92, 93, 96, 99, 101, 103, 108], "cut": [3, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 35, 105, 106, 110], "underestim": 3, "few": [3, 9, 10, 71, 85, 97, 99, 103, 104, 105, 106, 110], "4": [3, 4, 5, 10, 11, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 50, 51, 58, 67, 68, 70, 71, 73, 76, 83, 98, 99, 104, 109, 110], "detail": [3, 4, 5, 10, 13, 17, 19, 36, 39, 40, 44, 45, 51, 56, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 79, 80, 81, 85, 86, 90, 97, 99, 100, 104, 106, 110], "num_issu": [3, 7, 43, 90, 91, 92, 93, 95, 96, 97, 100, 101], "calibrate_confident_joint": 3, "up": [3, 7, 10, 20, 29, 30, 33, 46, 51, 53, 62, 63, 89, 98, 99, 105, 108, 110], "p_": [3, 39, 46], "pair": [3, 5, 10, 39, 46, 101], "v": [3, 10, 43, 64, 65, 67, 73, 91, 92, 102, 104, 105, 106, 107], "rest": [3, 5, 7, 9, 10, 12, 38, 64, 65, 67, 75, 88, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 108], "fashion": [3, 5, 77, 88], "2x2": 3, "incorrectli": [3, 39, 64, 65, 68, 95, 100, 110], "calibrated_cj": 3, "c": [3, 10, 57, 58, 65, 73, 85, 88, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108], "whose": [3, 4, 5, 10, 31, 40, 44, 49, 54, 58, 63, 67, 70, 76, 79, 83, 84, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 105, 106, 109, 110], "truli": [3, 106, 109], "estimate_joint": [3, 39, 101], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 65, 71, 101, 105, 107, 109, 110], "return_indices_of_off_diagon": 3, "frequenc": [3, 29, 63, 64, 71, 80, 105, 106], "done": [3, 10, 62, 75, 91, 99, 101, 104, 106, 107], "overfit": [3, 10, 68, 71, 88, 90, 91, 92, 93, 95, 96, 107], "classifict": 3, "singl": [3, 5, 9, 10, 15, 29, 39, 40, 44, 45, 51, 52, 59, 63, 64, 70, 71, 72, 73, 83, 88, 90, 91, 97, 99, 101, 104, 105], "baselin": [3, 40, 46, 89, 106, 108], "proxi": 3, "union": [3, 5, 15, 29, 51, 54, 55, 56, 59, 60, 65, 71, 75, 83, 99], "tupl": [3, 34, 40, 44, 45, 49, 50, 52, 54, 58, 59, 63, 65, 71, 79, 81, 83, 84, 90, 110], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 43, 49, 54, 55, 63, 72, 77, 79, 85, 89, 93, 97, 99, 100, 109], "practic": [3, 88, 89, 92, 93, 100, 101, 106, 108], "complet": [3, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "gist": 3, "cj_ish": 3, "guess": [3, 49, 101, 103], "8": [3, 5, 7, 8, 50, 51, 52, 58, 67, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 103, 104, 105, 106, 108, 109, 110], "parallel": [3, 46, 71, 81, 98], "again": [3, 62, 88, 99, 106], "simplifi": [3, 17, 99], "understand": [3, 9, 10, 39, 64, 71, 92, 97, 101, 102, 108, 109, 110], "100": [3, 4, 40, 44, 54, 55, 57, 72, 73, 88, 89, 91, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 110], "optim": [3, 40, 41, 44, 62, 88, 89, 92, 93, 95, 96, 97, 98, 101, 103, 104, 106, 108], "speed": [3, 46, 89, 98, 99, 108], "dtype": [3, 26, 28, 29, 34, 40, 44, 58, 59, 67, 83, 90, 97, 100, 105], "enumer": [3, 40, 44, 90, 91, 92, 93, 97, 110], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 44, 51, 59, 83, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "num_confident_bin": 3, "argmax": [3, 46, 73, 77, 80, 90, 97, 99, 101, 105, 106, 109], "elif": 3, "estimate_lat": 3, "py_method": [3, 49], "cnt": [3, 49], "1d": [3, 5, 13, 15, 19, 35, 43, 46, 51, 52, 54, 59, 60, 67, 76, 88, 90, 97], "eqn": [3, 49], "margin": [3, 46, 49, 51, 73], "marginal_p": [3, 49], "shorthand": [3, 13, 16], "proport": [3, 10, 39, 64, 101, 107], "poorli": [3, 49, 88, 97], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 101], "variabl": [3, 7, 17, 30, 59, 75, 76, 90, 91, 95, 101, 104, 108], "exact": [3, 10, 49, 54, 88, 91, 92, 93, 95, 97, 100], "within": [3, 4, 5, 10, 14, 18, 35, 40, 41, 44, 45, 47, 65, 70, 79, 81, 83, 91, 92, 93, 99, 105, 109], "percent": 3, "often": [3, 39, 49, 64, 99, 101, 107, 109], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 59, 60, 71, 88, 89, 90, 91, 93, 95, 96, 99, 100, 104, 105, 106, 108], "wai": [3, 5, 10, 54, 62, 85, 86, 88, 89, 90, 91, 92, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107], "pro": 3, "con": 3, "pred_proba": [3, 107], "combin": [3, 39, 91, 93, 97, 98, 99, 100, 101, 107, 108], "becaus": [3, 10, 49, 55, 59, 70, 96, 97, 99, 100, 101, 103, 105, 107], "littl": [3, 43, 98, 105, 110], "uniform": [3, 73, 98, 99, 101], "20": [3, 7, 45, 84, 90, 93, 96, 97, 98, 99, 100, 101, 105, 108, 109, 110], "Such": [3, 93, 106], "bound": [3, 26, 28, 40, 44, 58, 67, 68, 70, 71, 105], "reason": [3, 10, 25, 40, 44, 55, 72], "comment": [3, 58, 97, 110], "end": [3, 5, 40, 44, 56, 71], "file": [3, 5, 15, 42, 43, 61, 71, 88, 90, 91, 95, 96, 98, 99, 105, 106, 109, 110], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 101], "handl": [3, 5, 7, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 43, 44, 54, 55, 56, 86, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 101, 104, 106, 108, 109, 110], "five": [3, 68, 71, 101, 105], "estimate_cv_predicted_prob": [3, 101], "estimate_noise_matric": 3, "get_confident_threshold": [3, 42, 43], "amongst": [3, 10, 100, 105], "confident_threshold": [3, 10, 25, 26, 43, 72], "point": [4, 5, 7, 9, 10, 21, 29, 40, 44, 54, 56, 85, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "valuat": [4, 9, 21], "help": [4, 39, 40, 44, 71, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 106, 108, 109, 110], "u": [4, 88, 89, 90, 91, 93, 95, 97, 99, 101, 103, 104, 107, 108, 109, 110], "assess": [4, 10, 97, 100, 105], "contribut": [4, 10, 21, 97, 105], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 13, 19, 21, 22, 29, 31, 34, 47, 53, 95, 97], "metric": [4, 5, 10, 21, 22, 24, 29, 31, 34, 47, 53, 54, 56, 57, 59, 62, 71, 72, 88, 89, 90, 93, 95, 96, 97, 100, 101, 108], "10": [4, 10, 21, 22, 26, 29, 31, 34, 40, 41, 54, 71, 72, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "shaplei": [4, 10, 21], "nearest": [4, 5, 10, 13, 19, 26, 29, 31, 53, 54, 55, 56, 57, 72, 92, 96, 97, 106], "neighbor": [4, 5, 10, 13, 19, 21, 26, 29, 31, 47, 54, 55, 56, 57, 72, 91, 92, 93, 95, 96, 97, 99, 106], "knn": [4, 10, 13, 16, 21, 29, 31, 34, 53, 54, 55, 56, 57, 72, 95, 106], "graph": [4, 5, 10, 13, 16, 19, 21, 29, 34, 53, 54], "calcul": [4, 10, 21, 29, 43, 51, 53, 54, 57, 63, 67, 68, 70, 71, 72, 75, 79, 93, 98, 100], "directli": [4, 5, 10, 13, 17, 19, 36, 37, 43, 56, 62, 63, 89, 92, 96, 97, 99, 100, 104, 105, 108], "lowest": [4, 10, 63, 71, 92, 93, 95, 97, 99, 100, 103, 104, 105, 109], "fall": [4, 10, 70, 79, 83, 101, 106], "flag": [4, 10, 25, 29, 46, 51, 64, 65, 68, 75, 85, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 106, 108, 109], "approxim": [4, 10, 21, 43, 56, 72, 97, 103], "top": [4, 5, 10, 39, 43, 45, 46, 59, 65, 68, 71, 73, 80, 84, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 104, 105, 106, 108, 110], "found": [4, 5, 7, 10, 13, 16, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 106, 108, 110], "arxiv": [4, 21, 101], "ab": [4, 21, 101, 105], "1908": 4, "08619": 4, "1911": [4, 21], "07128": [4, 21], "embed": [4, 5, 10, 13, 19, 72, 85, 89, 90, 91, 92, 95, 96, 97, 100, 101, 104, 108], "represent": [4, 5, 10, 13, 19, 37, 40, 44, 52, 54, 65, 85, 89, 90, 91, 92, 93, 96, 99, 100, 101, 106], "suppli": [4, 104, 105, 108], "2d": [4, 5, 13, 19, 35, 43, 51, 52, 54, 58, 59, 63, 88, 90, 97, 104], "num_exampl": [4, 5, 13, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36, 39, 64, 90, 91, 92, 93, 95, 96, 100, 101], "num_featur": [4, 5, 13, 19, 40, 44, 62], "distanc": [4, 5, 10, 13, 19, 21, 29, 31, 34, 53, 54, 55, 56, 57, 70, 72, 95, 97, 106], "construct": [4, 5, 7, 10, 13, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 40, 44, 51, 53, 54, 56, 62, 97, 100], "nearestneighbor": [4, 5, 10, 21, 54, 56, 72, 95, 106], "cosin": [4, 10, 54, 55, 57, 72, 97, 106], "dim": [4, 72, 93, 109], "euclidean": [4, 5, 10, 54, 55, 57, 70, 72, 95], "dimension": [4, 29, 55, 59, 90, 101, 106], "scikit": [4, 44, 55, 56, 59, 72, 85, 88, 89, 90, 91, 92, 95, 96, 97, 99, 108], "fewer": [4, 10, 46, 59, 72, 97, 105], "stabl": [4, 14, 18, 24, 27, 32, 42, 47, 56, 59, 61, 72, 86, 90, 91, 92, 93, 95, 96, 100, 101], "exce": [4, 54, 93, 97], "transform": [4, 10, 35, 51, 54, 57, 59, 72, 73, 88, 89, 92, 93, 96, 97, 100, 106, 110], "rel": [4, 10, 39, 54, 63, 64, 72, 91, 92, 93, 95, 96, 100, 101, 106], "adjust": [4, 41, 46, 54, 67, 72, 73, 85, 97, 100, 101], "closer": [4, 10, 70, 97, 105], "highli": [4, 92, 93], "influenti": 4, "posit": [4, 5, 10, 40, 44, 57, 59, 71, 97, 98, 106], "convers": 4, "neg": [4, 10, 70, 71, 91, 92, 97, 98], "valueerror": [4, 5, 13, 15, 16, 37, 48, 51, 54, 57, 99], "neither": [4, 5, 10, 17, 55, 105], "nor": [4, 5, 10, 17], "larger": [4, 21, 55, 75, 77, 79, 93, 96, 98, 99], "55": [4, 58, 97, 98, 105, 108], "525": 4, "unifi": 5, "audit": [5, 9, 13, 15, 16, 19, 90, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 108], "kind": [5, 6, 7, 10, 97, 98], "addit": [5, 7, 9, 12, 13, 16, 36, 38, 40, 44, 51, 54, 56, 60, 63, 71, 80, 81, 88, 89, 90, 91, 95, 96, 97, 100, 101, 103, 106, 107], "depend": [5, 7, 9, 12, 13, 15, 16, 38, 42, 46, 48, 59, 61, 65, 72, 75, 76, 85, 97, 107], "instal": [5, 7, 9, 12, 38, 40, 42, 43, 44, 46, 61, 62, 77, 79, 97], "pip": [5, 7, 9, 12, 38, 62, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "development": [5, 7, 9, 12, 38], "git": [5, 7, 9, 12, 38, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "github": [5, 7, 9, 12, 38, 40, 41, 59, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 108], "com": [5, 7, 9, 12, 38, 40, 41, 43, 48, 59, 72, 85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "egg": [5, 7, 9, 12, 38, 85, 98], "label_nam": [5, 7, 8, 10, 11, 15, 21, 34, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 105, 108], "image_kei": [5, 10, 13, 93, 97], "interfac": [5, 9, 10, 56, 85, 88, 89, 92, 95, 96, 98, 99, 100, 101, 104, 106, 108], "librari": [5, 10, 44, 56, 68, 71, 72, 85, 89, 91, 96, 97, 98, 99], "goal": [5, 108], "track": [5, 7, 16, 17, 85, 91, 98, 99, 101], "intermedi": [5, 9, 92], "statist": [5, 10, 13, 16, 25, 29, 39, 63, 64, 71, 92, 95, 96, 97, 100, 101], "convert": [5, 10, 15, 37, 40, 44, 52, 57, 60, 63, 70, 79, 83, 86, 89, 90, 93, 96, 97, 98, 99, 100, 103, 104, 105], "hug": [5, 10, 15, 93], "face": [5, 10, 15, 19, 93, 98, 104], "kei": [5, 7, 10, 13, 15, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 51, 63, 64, 70, 72, 91, 92, 93, 96, 99, 101, 103, 105], "string": [5, 10, 13, 15, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 55, 59, 63, 64, 76, 80, 83, 84, 89, 95, 96, 97, 99, 103, 104, 110], "dictionari": [5, 7, 10, 13, 15, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 40, 44, 50, 59, 63, 64, 67, 68, 70, 71, 91, 92, 95, 96, 101, 103, 104, 105], "path": [5, 15, 40, 43, 44, 71, 90, 91, 97, 99, 105], "local": [5, 7, 10, 15, 40, 41, 44, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "text": [5, 7, 10, 15, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 45, 51, 72, 81, 83, 84, 85, 87, 91, 92, 94, 98, 99, 100, 101, 102, 103, 106], "txt": [5, 15, 110], "csv": [5, 15, 88, 89, 95, 96, 100, 108], "json": [5, 15], "hub": [5, 15], "multiclass": [5, 15, 18, 51, 59, 63, 104], "regress": [5, 7, 10, 11, 13, 15, 17, 19, 24, 33, 35, 37, 89, 91, 92, 96, 102, 103, 106], "multilabel": [5, 10, 11, 15, 17, 18, 24, 28, 35, 37, 52, 104], "imag": [5, 9, 13, 39, 44, 68, 70, 71, 72, 77, 79, 80, 85, 91, 92, 94, 98, 99, 100, 102, 103, 104, 105, 107, 109], "field": [5, 10, 40, 44], "themselv": [5, 88, 89, 97, 108], "pil": [5, 93], "cleanvis": [5, 10, 13, 97], "level": [5, 10, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36, 39, 54, 58, 81, 83, 92, 93, 99, 102, 104, 109], "load_dataset": [5, 15, 93], "glue": 5, "sst2": 5, "properti": [5, 9, 13, 15, 16, 37, 40, 44, 97], "has_label": [5, 15], "class_nam": [5, 15, 23, 39, 45, 64, 71, 80, 84, 85, 98, 101, 105, 109, 110], "empti": [5, 15, 49, 63, 92, 97, 99, 104], "find_issu": [5, 6, 7, 8, 10, 11, 13, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 85, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_typ": [5, 6, 7, 8, 10, 11, 13, 16, 17, 19, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "sort": [5, 13, 19, 43, 46, 51, 63, 65, 68, 70, 71, 73, 79, 81, 83, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 104, 105, 108, 109, 110], "common": [5, 10, 13, 16, 19, 85, 92, 94, 97, 98, 99, 100, 101, 104, 105, 109], "real": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "world": [5, 13, 19, 85, 91, 92, 97, 99, 100, 101, 103, 108, 109], "interact": [5, 13, 19, 96, 99], "thereof": [5, 13, 19], "insight": [5, 13, 19, 71, 103], "best": [5, 9, 10, 13, 19, 50, 63, 73, 88, 89, 91, 92, 93, 95, 97, 99, 100, 103, 104, 106, 107, 108, 110], "properli": [5, 10, 43, 50, 54, 59, 60, 77, 90, 91, 92, 93, 95, 96, 99, 100, 101, 104, 106, 108, 109], "respect": [5, 40, 44, 68, 71, 90, 91, 92, 93, 95, 96, 100, 101, 104, 105], "lexicograph": [5, 50, 59, 90, 91, 92, 93, 95, 96, 100, 101, 104], "squar": [5, 59, 75, 98, 108], "csr": [5, 54, 97], "evenli": 5, "omit": [5, 70, 71, 93, 97, 105], "itself": [5, 35, 40, 44, 54, 97, 105], "three": [5, 10, 39, 63, 64, 75, 80, 88, 90, 91, 92, 95, 98, 101, 103, 107, 108, 109, 110], "indptr": [5, 97], "wise": 5, "start": [5, 7, 10, 37, 40, 41, 44, 51, 85, 104, 110], "th": [5, 10, 45, 50, 58, 59, 63, 65, 68, 70, 71, 72, 81, 83, 84, 96, 104, 105, 110], "ascend": [5, 39, 64, 93, 101], "segment": [5, 77, 79, 80, 102], "reflect": [5, 10, 54, 88, 89, 95, 96, 100, 103, 105, 106, 108], "maintain": [5, 62], "kneighbors_graph": [5, 21, 56, 95], "illustr": [5, 97], "todens": 5, "second": [5, 51, 59, 71, 73, 91, 95, 99, 101, 110], "duplic": [5, 9, 24, 25, 40, 44, 54, 85, 91, 97, 100, 101, 108], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 63, 97, 99, 103, 110], "unspecifi": [5, 13, 19, 46, 65], "interest": [5, 13, 19, 25, 80, 84, 88, 89, 96, 97, 100, 101, 108, 109, 110], "constructor": [5, 10, 11, 13, 19, 26, 33, 54, 56], "issuemanag": [5, 9, 13, 16, 17, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 36], "respons": [5, 13, 19, 25, 56, 75, 76, 97, 98, 108, 110], "random_st": [5, 88, 90, 91, 92, 93, 97, 100, 101, 104, 106], "lab": [5, 6, 8, 10, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 43, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 108], "comprehens": [5, 85, 93, 97, 100, 104, 108], "nbr": 5, "n_neighbor": [5, 10, 21, 54, 56, 72, 97], "mode": [5, 12, 21, 40, 43, 44, 95, 106], "4x4": 5, "float64": [5, 29, 40, 44, 83], "compress": [5, 10, 54, 59, 77, 79, 97], "toarrai": [5, 54, 97], "NOT": [5, 43, 96], "23606798": 5, "41421356": [5, 54], "configur": [5, 19, 51, 92], "suppos": [5, 10, 68, 88, 89, 106, 108], "who": [5, 70, 88, 95, 97, 101, 110], "manag": [5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 62, 91, 99], "clean_learning_kwarg": [5, 10, 11, 26, 33, 99, 108], "labelissuemanag": [5, 10, 17, 24, 26], "prune_method": [5, 86], "prune_by_noise_r": [5, 46, 65, 101], "report": [5, 7, 10, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 64, 84, 85, 90, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108, 110], "include_descript": [5, 13, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 36], "show_summary_scor": [5, 13, 36, 97, 100], "show_all_issu": [5, 13, 36, 97, 100], "summari": [5, 7, 13, 16, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 45, 61, 62, 64, 69, 78, 79, 81, 82, 83, 86, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 105, 108, 109, 110], "show": [5, 7, 29, 40, 44, 50, 59, 71, 80, 84, 88, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 106, 108, 109, 110], "suffer": [5, 10, 13, 16, 25, 65, 73, 84, 97, 110], "onc": [5, 10, 25, 39, 40, 44, 88, 91, 99, 100, 101, 104, 105], "familiar": [5, 97], "overal": [5, 7, 10, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 45, 51, 63, 64, 67, 70, 71, 75, 79, 80, 81, 83, 85, 86, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 103, 105, 110], "sever": [5, 7, 10, 13, 15, 16, 25, 40, 43, 44, 46, 67, 70, 72, 73, 79, 83, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 105, 106, 110], "compar": [5, 63, 72, 83, 91, 92, 95, 97, 100, 101, 105], "issue_summari": [5, 7, 10, 13, 16, 97], "With": [5, 9, 10, 43, 89, 96, 99, 101, 103, 108, 109, 110], "usag": [5, 43, 62], "usual": [5, 15, 35, 36, 93, 103, 108], "ti": [5, 63], "exhibit": [5, 7, 10, 13, 16, 80, 90, 91, 92, 93, 95, 96, 100, 101, 105], "ie": [5, 75], "likelihood": [5, 10, 43, 45, 46, 65, 70, 72, 73, 77, 81, 97], "wherea": [5, 10, 59, 65, 88, 89, 97, 107], "outlier": [5, 9, 11, 17, 24, 25, 34, 47, 54, 73, 85, 91, 92, 97, 100, 101, 102, 108], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 101, 108], "global": [5, 7, 10, 25, 40, 44, 98], "non_iid": [5, 10, 11, 17, 29, 92, 93, 95, 96, 97, 100, 101], "hypothesi": [5, 97], "iid": [5, 7, 9, 29, 85, 95, 100, 101], "never": [5, 90, 100, 101, 104, 106, 107], "someth": [5, 7, 10, 40, 44, 73, 105], "123": [5, 91, 92], "456": [5, 88, 89, 90], "nearest_neighbor": 5, "7": [5, 10, 51, 52, 62, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 103, 104, 105, 106, 108, 109, 110], "9": [5, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 45, 51, 52, 67, 81, 83, 88, 89, 90, 91, 92, 95, 96, 97, 98, 101, 103, 104, 105, 106, 108, 109, 110], "distance_to_nearest_neighbor": [5, 11, 91, 92, 93, 95, 96, 100, 101], "789": 5, "get_issu": [5, 10, 13, 16, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 104, 108], "issue_nam": [5, 6, 7, 10, 13, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 90, 91, 92, 93, 95, 96, 97, 100, 101], "focu": [5, 10, 13, 16, 96, 97, 100, 109, 110], "full": [5, 10, 13, 16, 43, 62, 71, 93, 100, 110], "summar": [5, 13, 16, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 39, 64, 80, 84, 85, 109], "specific_issu": [5, 13, 16], "lie": [5, 10, 72, 73, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101], "get_issue_summari": [5, 10, 13, 16, 92, 97], "get_info": [5, 10, 13, 16, 92, 96, 97, 98], "yet": [5, 20, 30, 62, 98, 100, 103], "list_possible_issue_typ": [5, 17, 18], "regist": [5, 7, 17, 18, 20, 30, 40, 44, 91], "rtype": [5, 17, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44], "registri": [5, 17, 18], "list_default_issue_typ": [5, 17, 18], "folder": [5, 90, 91, 93], "load": [5, 15, 43, 71, 93, 98, 99, 100, 101, 105, 106, 109, 110], "futur": [5, 10, 25, 40, 44, 63, 85, 91, 96], "overwrit": [5, 91], "separ": [5, 39, 51, 67, 91, 92, 93, 97, 99, 100, 105, 107], "static": 5, "rememb": [5, 96, 99, 100, 101], "part": [5, 10, 40, 44, 46, 68, 70, 71, 90, 91, 97, 98, 100, 109, 110], "ident": [5, 10, 25, 59, 96, 97], "datalab": [6, 8, 11, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 85, 88, 89, 98, 100, 103, 108], "walk": [7, 100], "alongsid": [7, 13, 40, 44, 91, 99], "pre": [7, 8, 10, 40, 44, 85, 91, 92, 108], "runtim": [7, 40, 43, 44, 75, 77, 79, 90, 93, 99, 100], "issue_manager_factori": [7, 17, 91], "myissuemanag": [7, 17], "myissuemanagerforregress": 7, "decor": [7, 17], "ll": [7, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 107, 108, 110], "thing": [7, 44, 89, 97, 101, 108], "next": [7, 63, 85, 88, 89, 90, 95, 96, 97, 99, 103, 105, 108, 110], "dummi": 7, "randint": [7, 34, 51, 91, 92, 97], "mark": [7, 10, 86, 105, 106, 108], "regard": [7, 92, 100, 101], "rand": [7, 51, 54, 91, 92, 97], "is_": [7, 10, 91], "_issu": [7, 10, 91], "issue_score_kei": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "whole": [7, 10, 29, 40, 44, 92, 97], "make_summari": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 91], "popul": [7, 96, 100], "verbosity_level": [7, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "std": [7, 105], "raw_scor": 7, "bit": 7, "involv": [7, 43, 80, 84, 97, 99, 104], "intermediate_arg": 7, "min": [7, 51, 70, 83, 91, 99, 106], "sin_filt": 7, "sin": 7, "arang": [7, 97], "kernel": [7, 97], "affect": [7, 10, 40, 44, 55, 77, 83, 96, 97, 99], "easili": [7, 10, 49, 86, 88, 89, 90, 92, 95, 96, 100, 101, 103, 104, 106, 107, 108, 109], "hard": [7, 44, 85, 98, 106], "sai": [7, 10, 40, 44, 97, 104, 109], "anoth": [7, 10, 25, 39, 43, 55, 58, 70, 73, 89, 95, 96, 97, 99, 101, 103, 106], "try": [7, 9, 10, 43, 46, 62, 63, 77, 79, 85, 88, 89, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 106, 107, 108, 109], "won": [7, 40, 44, 91, 92, 99, 104], "issue_manag": [7, 10, 12, 13, 16, 18, 21, 22, 23, 26, 28, 29, 30, 31, 33, 34, 91], "instanti": [7, 19, 43, 62, 72, 89, 90, 92, 95], "477762": 7, "286455": 7, "term": [7, 10, 49, 59, 71, 90, 91, 92, 93, 95, 96, 100, 101], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 22, 31, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 105, 106, 108, 109, 110], "003042": 7, "058117": 7, "11": [7, 10, 62, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "121908": 7, "15": [7, 57, 62, 75, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "169312": 7, "17": [7, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 91, 92, 97, 98, 100, 101], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 34, 85, 100], "group": [8, 9, 29, 34, 85, 98, 100, 105, 110], "dbscan": [8, 10, 34], "hdbscan": 8, "etc": [8, 10, 25, 35, 40, 44, 49, 62, 63, 81, 85, 91, 92, 95, 96, 97, 99, 100, 101, 104, 108], "sensit": [8, 10, 57, 97, 100], "ep": [8, 34, 71], "radiu": 8, "min_sampl": [8, 34], "kmean": [8, 97], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 34, 97], "cluster_id": [8, 10, 11, 34, 97], "labels_": 8, "underperforming_group": [8, 10, 11, 17, 24, 92, 93, 95, 96, 97, 100, 101], "search": [9, 10, 23, 29, 30, 47, 53, 54, 55, 58, 75, 97, 99, 100, 107], "nondefault": 9, "Near": [9, 99], "imbal": [9, 24, 67, 72, 73, 92], "spuriou": [9, 13, 93], "correl": [9, 13, 93], "null": [9, 11, 17, 24, 92, 93, 96, 100, 101], "togeth": [9, 10, 49, 89, 91, 92, 93, 95, 96, 100, 101, 108, 110], "built": [9, 51, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "own": [9, 40, 42, 44, 56, 61, 67, 68, 71, 77, 81, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 108, 109, 110], "prerequisit": 9, "basic": [9, 44, 62, 97, 100, 106], "fulli": [9, 10, 40, 44, 62, 99], "platform": [9, 10, 85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 107, 108], "write": [9, 10], "code": [9, 10, 40, 44, 49, 59, 62, 85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 103, 104, 105, 106, 108, 109, 110], "being": [9, 10, 13, 16, 39, 40, 44, 46, 51, 58, 59, 73, 88, 95, 99, 100, 101, 108, 109], "100x": [9, 10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "faster": [9, 10, 43, 72, 75, 77, 79, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 108], "intellig": [9, 10, 100], "quickli": [9, 10, 41, 88, 90, 93, 95, 96, 99, 100, 104, 106, 107, 109, 110], "fix": [9, 10, 63, 88, 89, 92, 95, 96, 97, 98, 100, 101, 104, 106, 107, 108], "scientist": [9, 10], "million": [9, 10, 110], "thank": [9, 10], "ai": [9, 10, 85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 108, 110], "suggest": [9, 10, 39, 63, 64, 70, 89, 93, 96, 97, 99, 108], "power": [9, 10, 93, 98, 101, 110], "automl": [9, 10, 85, 88, 89, 92, 95, 96, 98, 99, 101, 104, 106, 107, 108], "system": [9, 10, 90, 93, 109], "foundat": [9, 10, 85, 88, 89, 92, 95, 96, 97, 98, 101, 104, 106, 107, 108], "improv": [9, 10, 63, 88, 89, 92, 93, 98, 99, 101, 102, 108, 109], "click": [9, 10, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "tune": [9, 10, 89, 90, 96, 98, 100, 106], "serv": [9, 10, 16, 19, 103], "auto": [9, 10, 88, 89, 92, 98, 99, 100, 108], "free": [9, 10, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "page": [10, 92, 99, 100, 101], "variou": [10, 16, 33, 42, 60, 61, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105], "why": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "matter": [10, 39, 64], "didn": [10, 97, 100], "plu": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "ye": [10, 11], "near_dupl": [10, 11, 17, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "class_imbal": [10, 11, 17, 23, 92, 93, 95, 96, 97, 100, 101], "data_valu": [10, 11, 17, 24, 97], "No": [10, 11, 88, 89, 96, 97, 99], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 70, 88, 89, 110], "issue_scor": 10, "atyp": [10, 72, 91, 92, 93, 95, 96, 100, 101, 106], "datapoint": [10, 34, 46, 51, 59, 73, 76, 85, 88, 89, 90, 91, 92, 95, 96, 99, 100, 107, 108], "is_issu": [10, 25], "primarili": 10, "former": [10, 40, 44], "investig": [10, 90, 97], "expertis": [10, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "interpret": [10, 98, 99, 101, 104, 108], "annot": [10, 39, 50, 63, 64, 65, 67, 68, 70, 71, 80, 83, 84, 85, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 105, 109], "dissimilar": [10, 95, 96], "preced": 10, "incorrect": [10, 70, 73, 76, 88, 90, 91, 92, 93, 95, 96, 97, 100, 101, 105, 108], "due": [10, 43, 46, 73, 77, 79, 90, 91, 92, 93, 95, 96, 97, 100, 101, 108], "appear": [10, 39, 50, 64, 65, 68, 76, 92, 93, 95, 96, 97, 100, 108, 109], "now": [10, 13, 43, 86, 88, 89, 90, 92, 97, 99, 100, 103, 105, 106, 108, 110], "token": [10, 45, 58, 79, 80, 81, 82, 83, 84, 99, 101, 102], "hamper": [10, 93, 98], "analyt": [10, 85, 97, 99, 103], "lead": [10, 70, 73, 93, 97, 100, 105], "draw": [10, 91, 92], "conclus": [10, 96], "let": [10, 40, 44, 72, 73, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 103, 104, 105, 106, 108, 109, 110], "sort_valu": [10, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 108], "head": [10, 88, 89, 90, 92, 93, 95, 96, 97, 98, 100, 101, 103, 108], "97": [10, 88, 98, 99, 100, 101, 105, 108, 110], "064045": 10, "58": [10, 88, 92, 97, 98, 101, 105], "680894": 10, "41": [10, 97, 98, 100, 105, 108], "746043": 10, "794894": 10, "98": [10, 98, 99, 100, 108], "802911": 10, "give": [10, 51, 73, 101, 103, 109], "li": [10, 72], "especi": [10, 88, 89, 93, 97, 99, 108], "veri": [10, 39, 64, 68, 70, 89, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 108], "rare": [10, 46, 71, 91, 92, 93, 95, 96, 99, 100, 101], "anomal": [10, 73, 91, 92, 93, 95, 96, 100, 101], "articl": [10, 43, 99], "blog": 10, "unexpect": [10, 40, 44, 96], "consequ": 10, "inspect": [10, 89, 90, 92, 93, 100, 101, 105, 108], "011562": 10, "62": [10, 97, 100, 101, 105, 108], "019657": 10, "22": [10, 90, 91, 93, 97, 98, 100, 101, 104, 105, 110], "035243": 10, "040907": 10, "42": [10, 51, 96, 97, 98, 105, 110], "056865": 10, "smaller": [10, 72, 104, 105], "extrem": [10, 13, 91, 92, 93, 95, 96, 97, 99, 100, 101], "record": [10, 40, 44, 90, 95, 108], "abbrevi": 10, "misspel": 10, "typo": [10, 84], "resolut": 10, "video": [10, 98], "audio": [10, 91, 92, 94, 99], "minor": [10, 58], "variat": 10, "translat": [10, 100], "d": [10, 57, 88, 95, 96, 97, 99, 100, 101, 104, 108, 110], "constant": [10, 34, 75], "median": [10, 33, 57], "question": [10, 25, 85, 101], "nearli": [10, 25, 92, 93, 95, 96], "awar": [10, 86, 101], "presenc": [10, 54, 56, 101], "36": [10, 97, 98, 100, 110], "066009": 10, "80": [10, 41, 88, 95, 100, 104, 108], "003906": 10, "093245": 10, "005599": 10, "27": [10, 95, 97, 98, 100, 101, 105, 110], "156720": 10, "009751": 10, "72": [10, 97, 98, 100, 101, 104, 108], "signific": [10, 88, 89, 92, 95, 96, 98, 100, 101, 104, 106, 108], "violat": [10, 85, 95, 96, 97, 100, 101], "assumpt": [10, 95, 96, 97, 100, 101], "changepoint": [10, 95, 96, 100, 101], "shift": [10, 54, 56, 95, 96, 100, 101], "drift": [10, 92, 95, 97, 100, 101], "autocorrel": [10, 95, 96, 100, 101], "almost": [10, 95, 96, 100, 101], "adjac": [10, 54, 95, 96, 100, 101], "tend": [10, 39, 49, 95, 96, 100, 101, 109, 110], "sequenti": [10, 40, 44, 62, 93], "pai": [10, 96, 97], "attent": [10, 97], "realli": [10, 89, 96, 100, 103, 109], "mere": 10, "highlight": [10, 80, 84, 91, 92, 95, 97, 109], "necessarili": [10, 63, 71, 96, 100, 101], "wrong": [10, 63, 68, 70, 86, 89, 91, 92, 96, 99, 100, 101, 105], "gap": 10, "b": [10, 21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 39, 58, 59, 83, 88, 95, 96, 97, 98, 99, 100, 101, 107, 110], "x1": [10, 68, 71, 105], "x2": [10, 68, 71, 105], "10th": 10, "100th": 10, "90": [10, 83, 88, 95, 100, 101, 107, 108], "similarli": [10, 40, 44, 91, 93, 95, 99, 100, 105], "associ": [10, 15, 19, 35, 37, 40, 44, 71, 103], "blogpost": 10, "proper": [10, 59, 63, 68, 71, 88, 93, 96, 99, 103, 105], "scenario": [10, 54, 56, 73, 91, 92], "underli": [10, 45, 56, 72, 81, 83, 110], "stem": [10, 72, 106], "evolv": 10, "influenc": 10, "act": [10, 70, 91], "accordingli": [10, 35, 54], "emploi": [10, 104, 106], "partit": [10, 107], "ahead": 10, "good": [10, 40, 44, 57, 62, 64, 70, 73, 77, 79, 80, 85, 93, 97, 100], "problem": [10, 35, 43, 51, 80, 85, 91, 92, 93, 96, 97, 99], "deploy": [10, 88, 89, 101, 108], "overlook": [10, 70, 105], "fact": 10, "thu": [10, 39, 44, 64, 88, 90, 95, 96, 100, 101, 107, 110], "diagnos": [10, 92, 99], "24": [10, 90, 97, 98, 100, 101, 103, 105, 108], "681458": 10, "37": [10, 91, 97, 98, 100], "804582": 10, "64": [10, 44, 88, 93, 95, 97, 101, 105], "810646": 10, "815691": 10, "78": [10, 88, 95, 98, 100, 101, 105, 108], "834293": 10, "Be": [10, 44], "cautiou": 10, "behavior": [10, 19, 39, 40, 44, 71, 99], "rarest": [10, 92, 100], "q": [10, 97, 105], "subpar": 10, "special": [10, 54, 58], "techniqu": [10, 105], "smote": 10, "asymmetr": [10, 39], "28": [10, 93, 96, 97, 98, 100, 101, 103, 110], "75": [10, 51, 91, 92, 97, 98, 100, 103, 104, 105, 108, 110], "33": [10, 40, 44, 97, 98, 100, 105], "68": [10, 88, 98, 100, 101, 105], "excess": [10, 93], "dark": [10, 97, 109], "bright": [10, 110], "blurri": [10, 93, 97], "lack": [10, 62, 97, 100], "unusu": [10, 105, 106], "discuss": [10, 99], "earlier": [10, 89, 110], "unintend": [10, 95, 96, 97], "relationship": [10, 39], "irrelev": 10, "exploit": 10, "fail": [10, 15], "unseen": 10, "hold": [10, 15], "aris": 10, "captur": [10, 39, 90, 105, 106, 109], "environment": 10, "preprocess": [10, 88, 89, 92, 95, 97, 106, 108], "systemat": [10, 80, 84, 103], "photograph": 10, "uncorrelated": [10, 97], "strongli": [10, 96, 97], "minu": [10, 73], "sole": [10, 75, 88, 91, 100, 103, 106], "review": [10, 88, 89, 92, 95, 96, 98, 99, 100, 101, 105, 108, 109, 110], "latch": 10, "onto": 10, "troublesom": 10, "spurious_correl": [10, 97], "correlations_df": [10, 97], "blurry_scor": [10, 97], "559": [10, 100], "dark_scor": [10, 93, 97], "808": 10, "light_scor": [10, 97], "723": [10, 95, 100], "odd_size_scor": [10, 97], "957": 10, "odd_aspect_ratio_scor": [10, 97], "835": 10, "grayscale_scor": [10, 97], "003": 10, "spurious": 10, "low_information_scor": [10, 93, 97], "688": [10, 100, 108], "categor": [10, 72, 87, 88, 91, 92, 94, 99, 100, 108], "characterist": [10, 39, 97], "grayscal": [10, 93, 97], "cluster": [10, 21, 34, 100], "slice": [10, 100], "poor": [10, 97, 100], "subpopul": [10, 100], "faq": [10, 85, 92, 93, 95, 96, 102], "get_self_confidence_for_each_label": [10, 51, 73], "r": [10, 43, 75, 91, 92, 97, 108, 109], "tabular": [10, 85, 87, 91, 92, 94, 97, 99, 100, 103], "encod": [10, 52, 71, 77, 80, 88, 89, 95, 96, 99, 100, 108, 109], "71": [10, 97, 98, 100, 101, 105, 108], "70": [10, 83, 95, 97, 100], "69": [10, 100, 101, 108], "subgroup": [10, 97], "wors": [10, 97, 103], "ratio": [10, 97], "miss": [10, 30, 40, 44, 59, 68, 70, 99, 100, 105, 108], "pattern": [10, 97], "isn": [10, 20, 30], "scalabl": 10, "sacrific": 10, "One": [10, 59, 72, 99], "quantif": 10, "39": [10, 89, 90, 91, 93, 96, 97, 98, 99, 100, 105, 108, 109, 110], "32": [10, 90, 91, 97, 98, 100, 103, 105], "valuabl": [10, 21, 97], "exert": [10, 92], "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, 24, 26, 33], "health_summari": [10, 26, 39, 85, 98], "health_summary_kwarg": 10, "tandem": [10, 98], "view": [10, 40, 44, 45, 46, 79, 81, 83, 85, 88, 89, 90, 91, 92, 95, 96, 98, 100, 101, 103, 104, 105, 106, 107, 108, 110], "strength": [10, 57, 71, 97], "scaling_factor": [10, 31, 57], "ood_kwarg": 10, "outofdistribut": [10, 31, 72, 106], "outsid": [10, 99, 104], "outlierissuemanag": [10, 17, 24, 31], "nearduplicateissuemanag": [10, 17, 22, 24], "noniidissuemanag": [10, 17, 24, 29], "num_permut": [10, 29], "permut": [10, 29], "significance_threshold": [10, 29], "signic": 10, "noniid": [10, 24], "classimbalanceissuemanag": [10, 17, 23, 24], "underperforminggroupissuemanag": [10, 17, 24, 34], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 34], "filter_cluster_id": [10, 24, 34], "clustering_kwarg": [10, 34], "nullissuemanag": [10, 17, 24, 30], "datavaluationissuemanag": [10, 17, 21, 24], "codeblock": 10, "demonstr": [10, 43, 54, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103, 104, 105, 107, 108, 109], "howev": [10, 40, 44, 54, 59, 88, 89, 90, 93, 95, 96, 97, 100, 103, 107, 109], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 64], "versa": [10, 64], "light": [10, 93, 97, 98, 105, 109], "29": [10, 93, 97, 98, 100, 103, 104, 105, 109, 110], "low_inform": [10, 93, 97], "odd_aspect_ratio": [10, 93, 97], "35": [10, 91, 97, 98, 100, 103, 104, 105], "odd_siz": [10, 93, 97], "doc": [10, 40, 44, 72, 85, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 106, 108, 110], "spurious_correlations_kwarg": 10, "enough": [10, 43, 59, 97, 99], "label_scor": [11, 26, 28, 33, 90, 91, 92, 93, 95, 96, 97, 100, 101, 104, 108], "is_outlier_issu": [11, 91, 92, 93, 95, 96, 97, 100, 101], "outlier_scor": [11, 31, 91, 92, 93, 95, 96, 97, 100, 101, 106], "is_near_duplicate_issu": [11, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_scor": [11, 22, 91, 92, 93, 95, 96, 97, 99, 100, 101], "near_duplicate_set": [11, 22, 24, 91, 92, 93, 95, 96, 99, 100, 101], "is_non_iid_issu": [11, 92, 95, 96, 97, 100, 101], "non_iid_scor": [11, 29, 92, 95, 96, 97, 100, 101], "is_class_imbalance_issu": [11, 92, 97, 100], "class_imbalance_scor": [11, 23, 92, 97, 100], "is_underperforming_group_issu": [11, 92, 97, 100], "underperforming_group_scor": [11, 34, 92, 97, 100], "is_null_issu": [11, 92, 97, 100], "null_scor": [11, 30, 92, 97, 100], "is_data_valuation_issu": [11, 97], "data_valuation_scor": [11, 21, 97], "studio": [12, 85, 88, 89, 92, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108], "data_issu": [12, 13, 18, 19, 36], "issue_find": [12, 18], "factori": [12, 18, 19], "model_output": [12, 18], "incorpor": [13, 86, 101], "vision": [13, 93], "create_imagelab": [13, 14], "huggingfac": [13, 90, 91, 92, 93, 99], "imagelabdataissuesadapt": [13, 14], "strategi": [13, 16, 51, 97, 99], "dataissu": [13, 16, 18, 19, 36], "_infostrategi": [13, 16], "basi": [13, 16], "filter_based_on_max_preval": 13, "max_num": 13, "collect_issues_from_imagelab": [13, 16], "collect_issues_from_issue_manag": [13, 16], "collect_statist": [13, 16], "reus": [13, 16, 25], "avoid": [13, 16, 40, 43, 44, 46, 54, 59, 65, 68, 71, 75, 77, 79, 91, 92, 99, 100], "recomput": [13, 16, 89], "weighted_knn_graph": [13, 16], "issue_manager_that_computes_knn_graph": [13, 16], "set_health_scor": [13, 16], "health": [13, 16, 26, 39, 64, 85], "correlationvisu": [13, 14], "visual": [13, 68, 69, 71, 88, 91, 92, 93, 108, 110], "title_info": 13, "ncol": [13, 93, 106], "cell_siz": 13, "correlationreport": [13, 14], "anyth": [13, 101], "imagelabreporteradapt": [13, 14], "get_report": [13, 36], "report_str": [13, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 36], "imagelabissuefinderadapt": [13, 14], "issuefind": [13, 18, 19, 36], "get_available_issue_typ": [13, 19], "handle_spurious_correl": [13, 14], "imagelab_issu": 13, "_": [13, 22, 23, 25, 26, 28, 29, 30, 33, 34, 51, 58, 59, 88, 90, 91, 93, 97, 98, 101, 104], "imagelab": [14, 16, 18], "except": [15, 40, 44, 62, 73, 91, 92, 93, 100, 103], "dataformaterror": [15, 18], "add_not": 15, "with_traceback": 15, "tb": 15, "__traceback__": 15, "datasetdicterror": [15, 18], "datasetdict": 15, "datasetloaderror": [15, 18], "dataset_typ": 15, "sublist": 15, "map_to_int": 15, "abc": [15, 25, 35], "is_avail": [15, 93], "central": [16, 110], "repositori": 16, "get_data_statist": [16, 18], "concret": 17, "subclass": [17, 40, 44, 72, 91], "regressionlabelissuemanag": [17, 24, 32, 33], "multilabelissuemanag": [17, 24, 27, 28], "from_str": [17, 37, 47, 51], "my_issu": 17, "logic": [17, 37, 43, 46, 77, 79, 100], "modeloutput": [18, 35], "multiclasspredprob": [18, 35], "regressionpredict": [18, 35], "multilabelpredprob": [18, 35], "instati": 19, "public": [19, 97, 100, 101, 105, 109, 110], "creation": [19, 44, 97], "execut": [19, 40, 44, 91, 99, 105], "coordin": [19, 68, 70, 71, 105, 110], "At": [19, 71, 99], "direct": [20, 30, 40, 44, 56, 62], "vstack": [21, 59, 93, 98, 99, 101, 103, 104], "25": [21, 29, 40, 51, 57, 92, 93, 97, 98, 100, 101, 103, 104, 105, 110], "classvar": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "short": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 58, 59], "item": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 40, 44, 59, 91, 92, 93, 99, 101, 103, 104], "some_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "additional_info_kei": [21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34], "default_threshold": [21, 24, 31], "collect_info": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "info_to_omit": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "compos": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34, 40, 44, 89, 96, 106], "is_x_issu": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "x_score": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_a": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b1": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "val_b2": [21, 22, 23, 25, 26, 28, 29, 31, 33, 34], "occurr": [22, 23, 25, 29, 30, 31, 34, 58], "median_nn_dist": 22, "bleed": [24, 27, 32, 42], "edg": [24, 27, 32, 42, 70, 85, 88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108, 110], "sharp": [24, 27, 32, 42], "get_health_summari": [24, 26], "ood": [24, 31, 72, 73, 106], "simplified_kolmogorov_smirnov_test": [24, 29], "outlier_cluster_label": [24, 34], "no_underperforming_cluster_id": [24, 34], "perform_clust": [24, 34], "get_underperforming_clust": [24, 34], "find_issues_with_predict": [24, 32, 33], "find_issues_with_featur": [24, 32, 33], "believ": [25, 109], "priori": [25, 101], "abstract": [25, 35], "applic": [26, 63, 97, 99, 101, 103, 110], "typevar": [26, 28, 40, 44, 58, 67, 70, 71], "scalartyp": [26, 28], "covari": [26, 28, 75, 108], "summary_dict": 26, "neighbor_histogram": 29, "non_neighbor_histogram": 29, "kolmogorov": 29, "smirnov": 29, "largest": [29, 43, 51, 54, 73, 77, 79, 105, 109], "empir": [29, 50, 63], "cumul": 29, "ecdf": 29, "histogram": [29, 95, 97, 108], "absolut": [29, 33], "trial": 29, "null_track": 30, "extend": [30, 52, 62, 93, 97, 100, 105, 106, 110], "superclass": 30, "arbitrari": [30, 39, 79, 83, 91, 106, 108], "prompt": 30, "address": [30, 89, 91, 92, 96, 99], "enabl": [30, 44, 56, 100], "37037": 31, "q3_avg_dist": 31, "iqr_avg_dist": 31, "median_outlier_scor": 31, "issue_threshold": 31, "multipli": [33, 57], "deleg": 33, "confus": [34, 35, 39, 40, 44, 46, 59, 71, 89, 110], "50": [34, 44, 97, 99, 100, 101, 103, 105, 106, 108], "keepdim": [34, 99], "signifi": 34, "absenc": 34, "int64": [34, 90, 100, 103], "npt": 34, "int_": 34, "id": [34, 63, 91, 93, 97, 99, 103], "unique_cluster_id": 34, "exclud": [34, 36, 45, 80, 84, 91, 110], "worst": [34, 51, 103], "performed_clust": 34, "worst_cluster_id": 34, "convent": [35, 37], "subject": [35, 37, 100], "meant": [35, 37], "Not": [35, 56], "mainli": [35, 106, 110], "content": [35, 72, 90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "fetch": [35, 43, 90, 92, 97, 99], "datset": 36, "enum": [37, 51], "qualnam": [37, 51], "boundari": [37, 51, 91, 92], "continu": [37, 62, 88, 89, 93, 96, 99, 103, 105, 108, 110], "binari": [37, 51, 59, 65, 67, 101, 110], "simultan": [37, 108], "task_str": 37, "is_classif": 37, "__contains__": [37, 47, 51], "member": [37, 40, 44, 51, 91], "typeerror": [37, 51], "12": [37, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 108, 109, 110], "__getitem__": [37, 47, 51], "match": [37, 39, 40, 44, 46, 51, 63, 64, 73, 91, 92, 93, 98, 105, 107, 109], "__iter__": [37, 47, 51], "__len__": [37, 47, 51], "alias": [37, 51], "is_regress": 37, "is_multilabel": 37, "overview": [39, 54, 88, 89, 90, 92, 93, 95, 96, 103, 105, 106, 108, 110], "modifi": [39, 40, 43, 44, 54, 56, 59, 99, 100, 101], "rank_classes_by_label_qu": [39, 92], "merg": [39, 54, 58, 85, 98, 99, 100, 110], "find_overlapping_class": [39, 99, 101], "problemat": [39, 64, 80, 84, 90, 105, 110], "unnorm": [39, 64, 101], "abov": [39, 40, 43, 44, 56, 59, 63, 70, 71, 73, 79, 83, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 107, 108, 109, 110], "model_select": [39, 51, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 106, 108], "cross_val_predict": [39, 44, 88, 89, 90, 91, 92, 95, 96, 97, 100, 101, 103, 107, 108], "get_data_labels_from_dataset": 39, "yourfavoritemodel": [39, 101], "cv": [39, 51, 88, 90, 91, 92, 95, 97, 100, 101, 103], "df": [39, 59, 84, 90, 97, 99], "overall_label_qu": [39, 64], "col": 39, "prob": [39, 58, 101, 107], "divid": [39, 64, 73], "label_nois": [39, 64], "human": [39, 98, 109, 110], "clearli": [39, 73, 93, 105, 109], "num": [39, 64, 98, 101], "overlap": [39, 85, 97, 98, 99, 101], "ontolog": 39, "publish": [39, 110], "therefor": [39, 73, 97, 100], "vehicl": [39, 98], "truck": [39, 97, 98, 106, 109], "intuit": [39, 64], "car": [39, 98, 105, 109], "frequent": [39, 63, 97, 99, 100, 108], "l": [39, 40, 44, 68, 70, 71], "class1": 39, "class2": 39, "dog": [39, 59, 64, 66, 80, 98, 99, 106, 107, 110], "cat": [39, 59, 64, 66, 98, 99, 106, 107], "co": [39, 40, 41], "noisy_label": [39, 91, 92, 104], "overlapping_class": 39, "descend": [39, 40, 44, 51, 64, 71], "overall_label_health_scor": [39, 64, 101], "half": [39, 40, 42, 44, 64, 98, 110], "health_scor": [39, 64], "classes_by_label_qu": [39, 92], "cnn": [40, 42, 44, 93], "cifar": [40, 41, 97, 98, 106], "teach": [40, 41], "bhanml": 40, "blob": [40, 97], "master": [40, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 104, 105, 106, 108], "call_bn": [40, 42], "bn": 40, "input_channel": 40, "n_output": 40, "dropout_r": 40, "top_bn": 40, "architectur": [40, 44], "shown": [40, 71, 90, 91, 92, 93, 95, 96, 99, 100, 101, 103, 106, 107, 109, 110], "forward": [40, 41, 42, 44, 93, 103], "overridden": [40, 44], "although": [40, 44, 72, 88, 95, 100], "recip": [40, 44], "afterward": [40, 44], "sinc": [40, 44, 48, 60, 64, 71, 79, 83, 99, 100, 103, 104, 105, 107, 110], "hook": [40, 44, 98], "silent": [40, 43, 44], "t_destin": [40, 42, 44], "__call__": [40, 42, 44, 47, 51], "add_modul": [40, 42, 44], "child": [40, 44], "fn": [40, 44, 71], "recurs": [40, 44, 51], "submodul": [40, 44, 53], "children": [40, 42, 44, 110], "nn": [40, 41, 44, 54, 93], "init": [40, 44, 101], "no_grad": [40, 44, 93, 106], "init_weight": [40, 44], "linear": [40, 44, 89, 93, 96], "fill_": [40, 44], "net": [40, 44, 90, 93, 98], "in_featur": [40, 44], "out_featur": [40, 44], "bia": [40, 44, 93], "tensor": [40, 41, 44, 90, 93, 106], "requires_grad": [40, 44], "bfloat16": [40, 42, 44], "cast": [40, 44, 90], "buffer": [40, 42, 44], "datatyp": [40, 44], "xdoctest": [40, 44], "undefin": [40, 44], "var": [40, 44], "buf": [40, 44], "20l": [40, 44], "1l": [40, 44], "5l": [40, 44], "call_super_init": [40, 42, 44], "immedi": [40, 44, 106], "compil": [40, 42, 44, 62], "cpu": [40, 42, 44, 46, 90, 93], "move": [40, 44, 51, 86, 98], "cuda": [40, 42, 44, 90, 93], "devic": [40, 44, 90, 93, 100], "gpu": [40, 44, 89, 90, 96], "live": [40, 44], "copi": [40, 44, 75, 88, 90, 91, 92, 95, 97, 99, 100, 104, 107, 108], "doubl": [40, 42, 44], "dump_patch": [40, 42, 44], "eval": [40, 42, 44, 93, 104, 106], "dropout": [40, 44], "batchnorm": [40, 44], "grad": [40, 44], "extra_repr": [40, 42, 44], "line": [40, 44, 85, 91, 97, 98, 103, 106, 110], "get_buff": [40, 42, 44], "target": [40, 41, 44, 75, 76, 97, 106, 108], "throw": [40, 44], "get_submodul": [40, 42, 44], "explan": [40, 44], "qualifi": [40, 44], "referenc": [40, 44], "attributeerror": [40, 44], "invalid": [40, 44, 96], "resolv": [40, 44, 97, 110], "get_extra_st": [40, 42, 44], "state_dict": [40, 42, 44], "set_extra_st": [40, 42, 44], "build": [40, 44, 54, 93, 97, 109], "picklabl": [40, 44], "serial": [40, 44], "backward": [40, 44, 93], "break": [40, 44, 93, 105], "pickl": [40, 44, 105], "get_paramet": [40, 42, 44], "net_b": [40, 44], "net_c": [40, 44], "conv": [40, 44], "conv2d": [40, 44, 93], "16": [40, 44, 51, 54, 62, 79, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 105, 106, 109, 110], "kernel_s": [40, 44], "stride": [40, 44], "200": [40, 44, 73, 97, 98, 105, 110], "diagram": [40, 44, 107], "degre": [40, 44], "queri": [40, 44, 54, 56, 92, 93, 97, 99, 100, 104], "named_modul": [40, 42, 44], "o": [40, 44, 57, 58, 90, 91, 92, 98, 99, 100, 101, 104, 105, 110], "transit": [40, 44], "ipu": [40, 42, 44], "load_state_dict": [40, 42, 44], "strict": [40, 44, 51], "persist": [40, 44], "strictli": [40, 44], "inplac": [40, 44, 97, 103], "preserv": [40, 44, 59], "namedtupl": [40, 44], "missing_kei": [40, 44], "unexpected_kei": [40, 44], "runtimeerror": [40, 44], "idx": [40, 44, 59, 60, 71, 91, 93, 97, 99, 100, 101, 103, 105, 106], "named_buff": [40, 42, 44], "prefix": [40, 44, 90, 110], "remove_dupl": [40, 44], "prepend": [40, 44], "running_var": [40, 44], "named_children": [40, 42, 44], "conv4": [40, 44], "conv5": [40, 44], "memo": [40, 44], "named_paramet": [40, 42, 44], "register_backward_hook": [40, 42, 44], "deprec": [40, 44, 48], "favor": [40, 44], "register_full_backward_hook": [40, 42, 44], "removablehandl": [40, 44], "register_buff": [40, 42, 44], "running_mean": [40, 44], "register_forward_hook": [40, 42, 44], "with_kwarg": [40, 44], "always_cal": [40, 44], "possibli": [40, 44, 88, 95], "fire": [40, 44, 98], "register_module_forward_hook": [40, 44], "regardless": [40, 44, 91, 92], "register_forward_pre_hook": [40, 42, 44], "And": [40, 44], "forward_pr": [40, 44], "register_module_forward_pre_hook": [40, 44], "gradient": [40, 44, 93, 95, 108], "grad_input": [40, 44], "grad_output": [40, 44], "technic": [40, 44], "caller": [40, 44], "register_module_full_backward_hook": [40, 44], "register_full_backward_pre_hook": [40, 42, 44], "backward_pr": [40, 44], "register_module_full_backward_pre_hook": [40, 44], "register_load_state_dict_post_hook": [40, 42, 44], "post": [40, 44, 54], "incompatible_kei": [40, 44], "modif": [40, 44, 54], "thrown": [40, 44], "register_modul": [40, 42, 44], "register_paramet": [40, 42, 44], "register_state_dict_pre_hook": [40, 42, 44], "keep_var": [40, 44], "requires_grad_": [40, 42, 44], "autograd": [40, 44], "freez": [40, 44, 89, 90, 96], "finetun": [40, 44], "gan": [40, 44], "share_memori": [40, 42, 44], "share_memory_": [40, 44], "destin": [40, 44], "shallow": [40, 44], "releas": [40, 44, 62, 86, 99], "design": [40, 44, 54], "ordereddict": [40, 44], "detach": [40, 44, 93], "non_block": [40, 44], "memory_format": [40, 44], "channels_last": [40, 44], "Its": [40, 44, 51, 64, 70], "complex": [40, 44, 100], "integr": [40, 44, 56, 85, 99], "asynchron": [40, 44], "host": [40, 44], "pin": [40, 44, 89, 96, 98], "desir": [40, 44, 54, 58, 71], "4d": [40, 44], "ignore_w": [40, 44], "determinist": [40, 44, 90], "1913": [40, 44], "3420": [40, 44], "5113": [40, 44], "2325": [40, 44], "env": [40, 44], "torch_doctest_cuda1": [40, 44], "gpu1": [40, 44], "1914": [40, 44], "5112": [40, 44], "2324": [40, 44], "float16": [40, 44], "cdoubl": [40, 44], "3741": [40, 44], "2382": [40, 44], "5593": [40, 44], "4443": [40, 44], "complex128": [40, 44], "6122": [40, 44], "1150": [40, 44], "to_empti": [40, 42, 44], "storag": [40, 44], "dst_type": [40, 44], "xpu": [40, 42, 44], "zero_grad": [40, 42, 44, 93], "set_to_non": [40, 44], "reset": [40, 44], "context": [40, 44, 105], "noisili": [41, 101], "han": 41, "2018": 41, "cifar_cnn": [41, 42], "loss_coteach": [41, 42], "y_1": 41, "y_2": 41, "forget_r": 41, "class_weight": 41, "logit": [41, 62, 93], "decim": [41, 59], "forget": [41, 51, 110], "rate_schedul": 41, "epoch": [41, 42, 44, 93, 99], "initialize_lr_schedul": [41, 42], "lr": [41, 42, 44], "001": [41, 73, 97, 99], "250": [41, 91, 92, 101, 105], "epoch_decay_start": 41, "schedul": 41, "beta": 41, "adam": 41, "adjust_learning_r": [41, 42], "alpha_plan": 41, "beta1_plan": 41, "forget_rate_schedul": [41, 42], "num_gradu": 41, "expon": 41, "tell": [41, 89, 93, 96, 101], "train_load": [41, 44], "model1": [41, 101], "optimizer1": 41, "model2": [41, 101], "optimizer2": 41, "dataload": [41, 93, 106], "parser": 41, "parse_arg": 41, "num_iter_per_epoch": 41, "print_freq": 41, "topk": 41, "top1": 41, "top5": 41, "test_load": 41, "offici": [42, 61, 97, 110], "wish": [42, 61, 100, 106, 109, 110], "adj_confident_thresholds_shar": [42, 43], "labels_shar": [42, 43], "pred_probs_shar": [42, 43], "labelinspector": [42, 43, 99], "get_num_issu": [42, 43], "get_quality_scor": [42, 43], "update_confident_threshold": [42, 43], "score_label_qu": [42, 43], "split_arr": [42, 43], "span_classif": 42, "display_issu": [42, 45, 78, 79, 80, 81, 82, 83, 84, 109, 110], "mnist_pytorch": 42, "get_mnist_dataset": [42, 44], "get_sklearn_digits_dataset": [42, 44], "simplenet": [42, 44], "batch_siz": [42, 43, 44, 77, 79, 93, 99, 106, 109], "log_interv": [42, 44], "momentum": [42, 44], "no_cuda": [42, 44], "test_batch_s": [42, 44, 93], "loader": [42, 44, 93], "set_predict_proba_request": [42, 44], "set_predict_request": [42, 44], "coteach": [42, 86], "mini": [43, 77, 79, 99], "low_self_confid": [43, 46, 65], "self_confid": [43, 46, 47, 51, 65, 67, 73, 81, 83, 88, 89, 99, 101], "conveni": [43, 56, 88, 89, 90, 96, 100], "script": 43, "labels_fil": [43, 99], "pred_probs_fil": [43, 99], "quality_score_kwarg": 43, "num_issue_kwarg": 43, "return_mask": 43, "variant": [43, 63, 109], "read": [43, 48, 92, 99, 101, 106, 110], "zarr": [43, 99], "memmap": [43, 109], "pythonspe": 43, "mmap": [43, 99], "hdf5": 43, "further": [43, 45, 64, 65, 67, 70, 71, 79, 80, 90, 97, 99, 100], "yourfil": 43, "npy": [43, 98, 99, 109], "mmap_mod": [43, 109], "tip": [43, 46, 62, 99], "save_arrai": 43, "your_arrai": 43, "disk": [43, 98, 99], "npz": [43, 110], "maxim": [43, 63, 77, 79, 100, 109], "multiprocess": [43, 46, 65, 77, 79, 93, 99], "linux": [43, 77, 79], "physic": [43, 46, 77, 79, 105], "psutil": [43, 46, 77, 79], "labels_arrai": [43, 60], "predprob": 43, "pred_probs_arrai": 43, "back": [43, 54, 71, 91, 99, 100, 105, 106], "store_result": 43, "becom": [43, 97, 106], "verifi": [43, 56, 99, 100, 103, 106], "long": [43, 63, 72, 100, 103], "chunk": [43, 107], "ram": [43, 98], "end_index": 43, "labels_batch": 43, "pred_probs_batch": 43, "batch_result": 43, "indices_of_examples_with_issu": [43, 99], "shortcut": 43, "encount": [43, 46, 77], "1000": [43, 90, 96, 99, 106], "aggreg": [43, 47, 51, 63, 67, 70, 73, 83, 99, 101, 103], "seen": [43, 99, 100, 106, 110], "far": [43, 63, 100], "label_quality_scor": [43, 67, 70, 73, 76, 101, 105], "method1": 43, "method2": 43, "normalized_margin": [43, 46, 47, 51, 65, 67, 73, 81, 83], "low_normalized_margin": [43, 46, 65], "issue_indic": [43, 70, 93], "update_num_issu": 43, "arr": [43, 99], "chunksiz": 43, "convnet": 44, "bespok": [44, 62], "download": [44, 90, 97, 99, 106], "mnist": [44, 85, 90, 98], "handwritten": 44, "digit": [44, 90, 98], "last": [44, 51, 68, 71, 91, 92, 99, 100, 103, 105, 110], "sklearn_digits_test_s": 44, "01": [44, 73, 75, 90, 97, 101, 104, 105], "templat": 44, "flexibli": 44, "among": [44, 63, 101], "test_set": 44, "overrid": 44, "train_idx": [44, 59, 106], "train_label": [44, 89, 100, 106], "span": [45, 100], "sentenc": [45, 58, 81, 83, 84, 89, 96], "token_classif": [45, 58, 81, 83, 84, 99], "encourag": [46, 65, 73, 76], "multilabel_classif": [46, 64, 65, 67, 73, 99, 104], "pred_probs_by_class": 46, "prune_count_matrix_col": 46, "rank_by_kwarg": [46, 65, 73, 101], "num_to_remove_per_class": [46, 65], "bad": [46, 54, 65, 70, 73, 96, 99], "seem": [46, 101, 104], "aren": 46, "confidence_weighted_entropi": [46, 47, 51, 65, 67, 73, 81, 83], "label_issues_idx": [46, 73, 100], "entropi": [46, 48, 50, 51, 72, 73], "prune_by_class": [46, 65, 101], "predicted_neq_given": [46, 65, 101], "prune_counts_matrix": 46, "smallest": [46, 73], "unus": 46, "number_of_mislabeled_examples_in_class_k": 46, "delet": [46, 85, 89, 99], "too": [46, 51, 54, 72, 93, 99, 100, 105], "thread": [46, 65], "window": [46, 98], "shorter": [46, 68], "find_predicted_neq_given": 46, "find_label_issues_using_argmax_confusion_matrix": 46, "remove_noise_from_class": [47, 59], "clip_noise_r": [47, 59], "clip_valu": [47, 59], "value_count": [47, 59, 99], "value_counts_fill_missing_class": [47, 59], "get_missing_class": [47, 59], "round_preserving_sum": [47, 59], "round_preserving_row_tot": [47, 59], "estimate_pu_f1": [47, 59], "confusion_matrix": [47, 59], "print_square_matrix": [47, 59], "print_noise_matrix": [47, 59, 101], "print_inverse_noise_matrix": [47, 59], "print_joint_matrix": [47, 59, 101], "compress_int_arrai": [47, 59], "train_val_split": [47, 59], "subset_x_i": [47, 59], "subset_label": [47, 59], "subset_data": [47, 59], "extract_indices_tf": [47, 59], "unshuffle_tensorflow_dataset": [47, 59], "is_torch_dataset": [47, 59], "is_tensorflow_dataset": [47, 59], "csr_vstack": [47, 59], "append_extra_datapoint": [47, 59], "get_num_class": [47, 59], "num_unique_class": [47, 59], "get_unique_class": [47, 59], "format_label": [47, 59], "smart_display_datafram": [47, 59], "force_two_dimens": [47, 59], "latent_algebra": [47, 86], "compute_ps_py_inv_noise_matrix": [47, 49], "compute_py_inv_noise_matrix": [47, 49], "compute_inv_noise_matrix": [47, 49], "compute_noise_matrix_from_invers": [47, 49], "compute_pi": [47, 49], "compute_pyx": [47, 49], "label_quality_util": 47, "get_normalized_entropi": [47, 48], "multilabel_util": [47, 104], "stack_compl": [47, 52], "get_onehot_num_class": [47, 52], "int2onehot": [47, 52, 104], "onehot2int": [47, 52, 104], "multilabel_scor": [47, 67], "classlabelscor": [47, 51], "exponential_moving_averag": [47, 51, 67], "softmin": [47, 51, 67, 70, 79, 83], "possible_method": [47, 51], "multilabelscor": [47, 51], "get_class_label_quality_scor": [47, 51], "multilabel_pi": [47, 51], "get_cross_validated_multilabel_pred_prob": [47, 51], "default_k": [47, 53, 54], "features_to_knn": [47, 53, 54], "construct_knn_graph_from_index": [47, 53, 54, 56], "create_knn_graph_and_index": [47, 53, 54], "correct_knn_graph": [47, 53, 54, 97], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [47, 53, 54], "correct_knn_distances_and_indic": [47, 53, 54], "high_dimension_cutoff": [47, 53, 55], "row_count_cutoff": [47, 53, 55], "decide_euclidean_metr": [47, 53, 55], "decide_default_metr": [47, 53, 55], "construct_knn": [47, 53, 56], "transform_distances_to_scor": [47, 57], "correct_precision_error": [47, 57], "token_classification_util": [47, 110], "get_sent": [47, 58, 110], "filter_sent": [47, 58, 110], "process_token": [47, 58], "merge_prob": [47, 58], "color_sent": [47, 58], "assert_valid_input": [47, 60], "assert_valid_class_label": [47, 60], "assert_nonempty_input": [47, 60], "assert_indexing_work": [47, 60], "labels_to_arrai": [47, 60], "labels_to_list_multilabel": [47, 60], "min_allowed_prob": 48, "wikipedia": 48, "activ": [48, 50, 62, 63, 85, 103], "towardsdatasci": 48, "cheatsheet": 48, "ec57bc067c0b": 48, "clip": [48, 59, 90, 97], "behav": 48, "unnecessari": [48, 99], "slightli": [48, 88, 89], "interv": [48, 51, 106], "herein": 49, "inexact": 49, "cours": [49, 100], "propag": 49, "throughout": [49, 59, 75, 84, 90, 103, 109, 110], "increas": [49, 57, 70, 72, 73, 90, 91, 97, 99, 103, 104, 110], "dot": [49, 83, 99], "true_labels_class_count": 49, "pyx": 49, "multiannot": 50, "assert_valid_inputs_multiannot": 50, "labels_multiannot": [50, 63], "ensembl": [50, 51, 63, 73, 88, 95, 99, 104, 106, 108], "allow_single_label": 50, "annotator_id": 50, "assert_valid_pred_prob": 50, "pred_probs_unlabel": [50, 63], "format_multiannotator_label": [50, 63, 103], "formatted_label": [50, 59], "old": [50, 59, 86, 98], "check_consensus_label_class": 50, "consensus_label": [50, 63, 103], "consensus_method": [50, 63], "consensu": [50, 63, 85, 102, 110], "establish": [50, 62, 89, 108], "compute_soft_cross_entropi": 50, "soft": [50, 98], "find_best_temp_scal": 50, "coarse_search_rang": [50, 75, 99], "fine_search_s": [50, 75, 99], "temperatur": [50, 51, 70, 79, 83], "scale": [50, 57, 88, 97, 98, 99, 106, 109], "factor": [50, 51, 57, 77, 79], "minim": [50, 70, 106], "temp_scale_pred_prob": 50, "temp": 50, "sharpen": [50, 98], "smoothen": 50, "get_normalized_margin_for_each_label": [51, 73], "get_confidence_weighted_entropy_for_each_label": [51, 73], "scorer": 51, "alpha": [51, 67, 70, 91, 92, 97, 101, 104, 108], "exponenti": 51, "ema": 51, "s_1": 51, "s_k": 51, "ema_k": 51, "accord": [51, 65, 95, 96, 101, 110], "formula": [51, 57], "_t": 51, "cdot": 51, "s_t": 51, "qquad": 51, "leq": 51, "_1": 51, "recent": [51, 110], "success": 51, "previou": [51, 54, 93, 95, 99, 105], "discount": 51, "s_ema": 51, "175": [51, 93, 100, 101, 105], "underflow": 51, "nan": [51, 63, 88, 95, 97, 100, 103, 108], "aggregated_scor": 51, "base_scor": [51, 100], "base_scorer_kwarg": 51, "aggregator_kwarg": [51, 67], "n_sampl": [51, 97], "n_label": 51, "class_label_quality_scor": 51, "452": 51, "new_scor": 51, "575": [51, 100], "get_label_quality_scores_per_class": [51, 66, 67], "ml_scorer": 51, "binar": [51, 52], "reformat": [51, 90], "wider": 51, "splitter": 51, "kfold": [51, 93], "onevsrestclassifi": [51, 104], "randomforestclassifi": [51, 101, 104], "n_split": [51, 93, 104], "pred_prob_slic": 52, "onehot": 52, "hot": [52, 65, 71, 77, 80, 88, 95, 98, 99, 108, 109], "onehot_matrix": 52, "pairwis": [53, 55, 72], "reli": [54, 72, 89, 90, 91, 92, 96, 105, 106, 108], "sklearn_knn_kwarg": 54, "correction_featur": 54, "discourag": 54, "flexibl": [54, 99], "manner": [54, 67, 88, 89, 97, 103, 108], "701": 54, "900": [54, 88, 95, 108], "436": [54, 100], "000": [54, 89, 93, 96, 97, 98, 110], "idea": [54, 73, 100, 105], "dens": [54, 62, 97], "33140006": 54, "76210367": 54, "correct_exact_dupl": 54, "mutual": [54, 64, 104], "vari": [54, 70, 92], "exact_duplicate_set": 54, "main": [54, 63], "front": [54, 98], "consider": 54, "capabl": [54, 85, 100], "come": [54, 59, 91, 92, 99, 109], "misidentif": 54, "corrected_dist": 54, "corrected_indic": 54, "sqrt": 54, "distant": 54, "suitabl": [55, 63, 88, 95, 97, 100], "slower": 55, "decid": [55, 63, 89, 96, 98, 103, 108, 110], "predefin": 55, "met": [55, 110], "euclidean_dist": [55, 72], "spatial": [55, 72], "decis": [55, 88, 91, 92, 100], "That": [55, 88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "cosine_dist": 55, "knn_kwarg": 56, "html": [56, 59, 68, 71, 72, 90, 91, 92, 93, 95, 96, 99, 100, 101], "kneighbor": 56, "metric_param": 56, "n_features_in_": 56, "effective_metric_params_": 56, "effective_metric_": 56, "n_samples_fit_": 56, "__sklearn_is_fitted__": 56, "conduct": 56, "is_fit": 56, "trail": 56, "underscor": 56, "avg_dist": 57, "exp": [57, 72, 73, 91], "dt": 57, "right": [57, 68, 71, 89, 96, 104, 105, 106], "pronounc": 57, "differenti": 57, "ly": 57, "rule": [57, 58, 85, 98], "thumb": 57, "ood_features_scor": [57, 72, 106], "88988177": 57, "80519832": 57, "toler": 57, "minkowski": 57, "noth": 57, "epsilon": 57, "sensibl": 57, "fixed_scor": 57, "readabl": 58, "lambda": [58, 90, 91, 99, 100, 103], "long_sent": 58, "headlin": 58, "charact": [58, 59], "s1": 58, "s2": 58, "processed_token": 58, "alecnlcb": 58, "entiti": [58, 85, 99, 110], "mapped_ent": 58, "unique_ident": 58, "loc": [58, 91, 92, 93, 95, 97, 110], "nbitbas": [58, 67], "probs_merg": 58, "0125": [58, 83], "0375": 58, "075": 58, "025": 58, "color": [58, 80, 91, 92, 95, 97, 101, 104, 106, 108, 109], "red": [58, 71, 91, 92, 97, 98, 101, 104, 105, 106, 109], "colored_sent": 58, "termcolor": 58, "31msentenc": 58, "0m": 58, "ancillari": 59, "class_without_nois": 59, "any_other_class": 59, "choos": [59, 73, 88, 95, 99, 101, 108], "tradition": 59, "new_sum": 59, "fill": 59, "major": [59, 63, 86, 93, 106], "versu": [59, 101], "obviou": 59, "cgdeboer": 59, "iteround": 59, "reach": 59, "prob_s_eq_1": 59, "claesen": 59, "f1": [59, 71, 96, 101], "BE": 59, "left_nam": 59, "top_nam": 59, "titl": [59, 91, 92, 97, 101, 104, 106], "short_titl": 59, "round_plac": 59, "pretti": [59, 101], "joint_matrix": 59, "num_possible_valu": 59, "holdout_idx": 59, "extract": [59, 72, 89, 90, 95, 96, 100, 103, 106, 109], "allow_shuffl": 59, "turn": [59, 85, 105], "shuffledataset": 59, "histori": 59, "pre_x": 59, "buffer_s": 59, "csr_matric": 59, "append": [59, 90, 93, 98, 99, 100, 101, 103, 104, 105, 106, 110], "bottom": [59, 68, 71, 97, 105], "to_data": 59, "from_data": 59, "taken": 59, "label_matrix": 59, "canon": 59, "displai": [59, 71, 80, 84, 89, 90, 95, 96, 97, 101, 110], "jupyt": [59, 90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "notebook": [59, 63, 90, 92, 98, 99, 100, 101, 103, 104, 105, 107, 109, 110], "consol": 59, "allow_missing_class": 60, "allow_one_class": 60, "length_x": 60, "labellik": 60, "labels_list": [60, 65], "keraswrappermodel": [61, 62, 85], "keraswrappersequenti": [61, 62], "tf": [62, 90], "legaci": 62, "newer": 62, "interim": 62, "advis": [62, 104], "stabil": [62, 72], "until": 62, "accommod": 62, "keraswrapp": 62, "huggingface_keras_imdb": 62, "unit": [62, 110], "model_kwarg": [62, 75], "compile_kwarg": 62, "sparsecategoricalcrossentropi": 62, "layer": [62, 89, 90, 96, 106], "my_keras_model": 62, "from_logit": 62, "declar": 62, "apply_softmax": 62, "analysi": 63, "analyz": [63, 85, 97, 101, 103, 104], "get_label_quality_multiannot": [63, 103], "vote": 63, "crowdsourc": [63, 85, 103], "dawid": [63, 103], "skene": [63, 103], "analog": [63, 98, 103], "chosen": [63, 73, 99, 103], "crowdlab": [63, 103], "unlabel": [63, 93, 103, 106, 109], "get_active_learning_scor": [63, 103], "activelab": [63, 103], "priorit": [63, 70, 105, 109, 110], "showcas": 63, "best_qual": 63, "quality_method": 63, "calibrate_prob": 63, "return_detailed_qu": 63, "return_annotator_stat": 63, "return_weight": 63, "label_quality_score_kwarg": 63, "did": [63, 64, 88, 89, 90, 95, 101, 103, 108], "majority_vot": 63, "broken": [63, 71, 98, 108], "highest": [63, 71, 91, 93, 100, 107], "0th": 63, "consensus_quality_scor": [63, 103], "annotator_agr": [63, 103], "reman": 63, "1st": 63, "2nd": [63, 77], "3rd": 63, "consensus_label_suffix": 63, "consensus_quality_score_suffix": 63, "suffix": 63, "emsembl": 63, "weigh": [63, 98], "agreement": [63, 103], "agre": 63, "prevent": [63, 99], "overconfid": [63, 107], "detailed_label_qu": [63, 103], "annotator_stat": [63, 103], "model_weight": 63, "annotator_weight": 63, "warn": 63, "labels_info": 63, "num_annot": [63, 103], "deriv": [63, 103], "quality_annotator_1": 63, "quality_annotator_2": 63, "quality_annotator_m": 63, "annotator_qu": [63, 103], "num_examples_label": [63, 103], "agreement_with_consensu": [63, 103], "worst_class": [63, 103], "trustworthi": [63, 103, 108], "get_label_quality_multiannotator_ensembl": 63, "weigtht": 63, "budget": 63, "retrain": [63, 89, 108], "active_learning_scor": 63, "active_learning_scores_unlabel": 63, "get_active_learning_scores_ensembl": 63, "henc": [63, 90, 91, 100, 103], "get_majority_vote_label": [63, 103], "event": 63, "lastli": [63, 95], "convert_long_to_wide_dataset": 63, "labels_multiannotator_long": 63, "wide": [63, 88, 89, 90], "labels_multiannotator_wid": 63, "common_multilabel_issu": [64, 66], "exclus": [64, 104], "rank_classes_by_multilabel_qu": [64, 66], "overall_multilabel_health_scor": [64, 66], "multilabel_health_summari": [64, 66], "classes_by_multilabel_qu": 64, "inner": [65, 79, 97], "find_multilabel_issues_per_class": [65, 66], "per_class_label_issu": 65, "label_issues_list": 65, "pred_probs_list": [65, 73, 93, 101], "anim": [66, 106], "rat": 66, "predat": 66, "pet": 66, "reptil": 66, "box": [68, 70, 71, 98, 105], "object_detect": [68, 70, 71, 105], "return_indices_ranked_by_scor": [68, 105], "overlapping_label_check": [68, 70], "suboptim": [68, 70], "locat": [68, 70, 97, 105, 109, 110], "bbox": [68, 71, 105], "image_nam": [68, 71], "y1": [68, 71, 105], "y2": [68, 71, 105], "later": [68, 71, 72, 89, 100, 110], "corner": [68, 71, 105], "xyxi": [68, 71, 105], "io": [68, 71, 90, 97, 98], "keras_cv": [68, 71], "bounding_box": [68, 71, 105], "detectron": [68, 71, 105], "detectron2": [68, 71, 105], "readthedoc": [68, 71], "en": [68, 71], "latest": [68, 71], "draw_box": [68, 71], "mmdetect": [68, 71, 105], "swap": [68, 70, 80, 84], "penal": [68, 70], "concern": [68, 70, 85, 92], "issues_from_scor": [69, 70, 78, 79, 80, 82, 83, 84, 105, 109, 110], "compute_overlooked_box_scor": [69, 70], "compute_badloc_box_scor": [69, 70], "compute_swap_box_scor": [69, 70], "pool_box_scores_per_imag": [69, 70], "object_counts_per_imag": [69, 71, 105], "bounding_box_size_distribut": [69, 71, 105], "class_label_distribut": [69, 71, 105], "get_sorted_bbox_count_idx": [69, 71], "plot_class_size_distribut": [69, 71], "plot_class_distribut": [69, 71], "get_average_per_class_confusion_matrix": [69, 71], "calculate_per_class_metr": [69, 71], "aggregation_weight": 70, "imperfect": [70, 99, 100], "chose": [70, 103, 105], "imperfectli": [70, 105], "dirti": [70, 73, 76, 108], "subtyp": 70, "badloc": 70, "nonneg": 70, "high_probability_threshold": 70, "auxiliary_input": [70, 71], "iou": [70, 71], "heavili": 70, "auxiliarytypesdict": 70, "pred_label": [70, 89], "pred_label_prob": 70, "pred_bbox": 70, "lab_label": 70, "lab_bbox": 70, "similarity_matrix": 70, "min_possible_similar": 70, "scores_overlook": 70, "low_probability_threshold": 70, "scores_badloc": 70, "accident": [70, 89, 95, 96, 99], "scores_swap": 70, "box_scor": 70, "image_scor": [70, 79, 109], "discov": [71, 92, 97, 110], "abnorm": [71, 93, 105], "auxiliari": [71, 106, 109], "_get_valid_inputs_for_compute_scor": 71, "object_count": 71, "down": 71, "bbox_siz": 71, "class_distribut": 71, "plot": [71, 91, 92, 97, 101, 104, 106, 108, 109], "sorted_idx": [71, 106], "class_to_show": 71, "hidden": [71, 106], "max_class_to_show": 71, "plt": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "matplotlib": [71, 80, 91, 92, 93, 97, 101, 104, 105, 106, 108], "pyplot": [71, 80, 91, 92, 93, 97, 101, 104, 106, 108], "prediction_threshold": 71, "overlai": [71, 105], "figsiz": [71, 91, 92, 93, 97, 101, 104, 106], "save_path": [71, 105], "blue": [71, 98, 101, 105], "overlaid": 71, "side": [71, 98, 105], "figur": [71, 97, 101, 104, 106, 108], "extens": [71, 101, 103], "png": [71, 105], "pdf": [71, 72], "svg": 71, "num_proc": [71, 93], "intersect": [71, 99], "tp": 71, "fp": 71, "ground": [71, 98, 101, 103, 108], "truth": [71, 101, 103, 108], "bias": [71, 97], "avg_metr": 71, "distionari": 71, "95": [71, 81, 83, 95, 98, 100, 101, 108], "per_class_metr": 71, "Of": 72, "find_top_issu": [72, 73, 106], "behind": [72, 101], "dist_metr": 72, "subtract": [72, 73], "renorm": [72, 73, 99], "least_confid": 72, "sum_": 72, "log": [72, 73, 86], "softmax": [72, 79, 83, 93], "literatur": 72, "gen": 72, "liu": 72, "lochman": 72, "zach": 72, "openaccess": 72, "thecvf": 72, "cvpr2023": 72, "liu_gen_pushing_the_limits_of_softmax": 72, "based_out": 72, "distribution_detection_cvpr_2023_pap": 72, "fit_scor": [72, 106], "ood_predictions_scor": 72, "pretrain": [72, 89, 90, 96, 100, 106], "adjust_confident_threshold": 72, "probabilist": [72, 88, 90, 91, 92, 95, 96, 106, 107], "order_label_issu": [73, 86], "whichev": [73, 107], "argsort": [73, 89, 93, 96, 101, 105, 106, 108], "max_": 73, "get_label_quality_ensemble_scor": [73, 99, 101], "weight_ensemble_members_bi": 73, "custom_weight": 73, "log_loss_search_t_valu": 73, "0001": [73, 98], "scheme": 73, "log_loss_search": 73, "log_loss": [73, 96], "1e0": 73, "1e1": 73, "1e2": 73, "2e2": 73, "quality_scor": [73, 106], "forth": 73, "top_issue_indic": 73, "rank_bi": [73, 86], "weird": [73, 84], "prob_label": 73, "max_prob_not_label": 73, "AND": [73, 96], "get_epistemic_uncertainti": [74, 75], "get_aleatoric_uncertainti": [74, 75], "corrupt": [75, 108], "linearregress": [75, 99, 108], "y_with_nois": 75, "n_boot": [75, 99], "include_aleatoric_uncertainti": [75, 99], "bootstrap": [75, 99, 108], "resampl": [75, 90, 99], "epistem": [75, 99, 106, 108], "aleator": [75, 99, 108], "model_final_kwarg": 75, "coars": 75, "thorough": [75, 99], "fine": [75, 89, 90, 96, 106], "grain": 75, "grid": [75, 100], "varianc": [75, 101], "epistemic_uncertainti": 75, "residu": [75, 76, 99], "deviat": [75, 105, 108], "aleatoric_uncertainti": 75, "outr": 76, "contin": 76, "raw": [76, 85, 86, 92, 93, 98, 99, 100, 103, 105, 106, 108], "aka": [76, 90, 101, 105, 108, 110], "00323821": 76, "33692597": 76, "00191686": 76, "semant": [77, 79, 80, 102], "pixel": [77, 79, 80, 93, 106, 109], "h": [77, 79, 80, 109], "height": [77, 79, 80, 109], "w": [77, 79, 80, 109], "width": [77, 79, 80, 109], "labels_one_hot": [77, 80, 109], "stream": [77, 106, 110], "downsampl": [77, 79, 109], "shrink": [77, 79], "divis": [77, 79, 91], "common_label_issu": [78, 80, 82, 84, 109, 110], "filter_by_class": [78, 80, 109], "segmant": [79, 80], "num_pixel_issu": [79, 109], "product": [79, 93, 97, 99, 100], "pixel_scor": [79, 109], "enter": 80, "legend": [80, 91, 92, 97, 104, 105, 108, 109], "colormap": 80, "background": [80, 97], "person": [80, 99, 105, 109, 110], "ambigu": [80, 84, 89, 90, 96, 98, 101, 110], "misunderstood": [80, 84], "issues_df": [80, 93], "class_index": 80, "issues_subset": [80, 84], "filter_by_token": [82, 84, 110], "token_score_method": 83, "sentence_score_method": 83, "sentence_score_kwarg": 83, "compris": [83, 84], "token_scor": [83, 110], "converg": 83, "toward": [83, 97], "_softmin_sentence_scor": 83, "sentence_scor": [83, 110], "token_info": 83, "02": [83, 91, 92, 97, 101, 105], "03": [83, 95, 97, 98, 100, 101, 105, 106, 110], "04": [83, 95, 97, 105], "08": [83, 97, 101, 105, 108, 110], "commonli": [84, 86, 91, 92, 104, 110], "But": [84, 96, 100, 101, 108, 110], "restrict": [84, 99], "reliabl": [85, 88, 90, 97, 99, 100, 103, 109], "thousand": 85, "imagenet": [85, 98], "popular": [85, 103, 105], "centric": [85, 93, 102], "minut": [85, 88, 89, 90, 95, 96, 98, 103, 104, 105, 108, 109, 110], "conda": 85, "feature_embed": [85, 106], "your_dataset": [85, 90, 91, 92, 93, 95, 96, 99], "column_name_of_label": [85, 90, 91, 92, 93, 95, 96], "tool": [85, 98, 101, 103], "catch": [85, 100], "dive": [85, 96, 97, 100], "plagu": [85, 92], "untrain": 85, "\u30c4": 85, "label_issues_info": [85, 92], "sklearn_compatible_model": 85, "framework": [85, 104, 105], "complianc": 85, "tag": [85, 104, 110], "sequenc": 85, "recognit": [85, 90, 99, 110], "train_data": [85, 88, 89, 106, 108], "gotten": 85, "test_data": [85, 88, 89, 101, 104, 106, 108], "deal": [85, 92, 97, 100], "feel": [85, 90, 92, 99], "ask": [85, 99], "slack": [85, 99], "project": [85, 100, 108], "welcom": 85, "commun": [85, 99], "guidelin": [85, 105], "piec": 85, "smart": [85, 88, 89, 92, 93, 95, 96, 98, 99, 101, 104, 106, 108], "edit": [85, 99, 100], "unreli": [85, 88, 90, 95, 96, 97, 100], "link": [85, 90, 98, 105], "older": 86, "outlin": 86, "substitut": [86, 100], "v2": [86, 88, 95], "get_noise_indic": 86, "psx": 86, "sorted_index_method": 86, "order_label_error": 86, "label_errors_bool": 86, "latent_estim": 86, "num_label_error": 86, "learningwithnoisylabel": 86, "neatli": 86, "organ": [86, 88, 95, 97, 98, 110], "reorgan": 86, "baseline_method": 86, "research": [86, 101], "polyplex": 86, "terminologi": 86, "label_error": 86, "quickstart": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 103, 104, 105, 106, 108, 109, 110], "sql": [88, 95], "databas": [88, 95], "excel": [88, 95], "parquet": [88, 95], "student": [88, 95, 100, 108, 110], "grade": [88, 95, 100, 108], "exam": [88, 95, 100, 108], "letter": [88, 95, 110], "hundr": [88, 95], "mistak": [88, 89, 93, 95, 96, 100], "extratreesclassifi": 88, "extratre": 88, "Then": [88, 89, 93, 99], "ranked_label_issu": [88, 89], "branch": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108], "standardscal": [88, 95, 100, 106], "labelencod": [88, 89, 100], "train_test_split": [88, 89, 91, 92, 106], "accuracy_scor": [88, 89, 90, 96, 100, 101], "grades_data": [88, 95], "read_csv": [88, 89, 95, 96, 97, 100, 108], "demo": [88, 92, 95, 104], "stud_id": [88, 95, 100], "exam_1": [88, 95, 100, 108], "exam_2": [88, 95, 100, 108], "exam_3": [88, 95, 100, 108], "letter_grad": [88, 95], "f48f73": [88, 95], "53": [88, 91, 92, 95, 97, 98, 100, 104, 105], "00": [88, 91, 92, 95, 97, 98, 100, 106], "77": [88, 91, 92, 95, 100, 105], "0bd4e7": [88, 95], "81": [88, 95, 96, 100, 105, 108, 110], "great": [88, 95, 98, 100], "particip": [88, 95, 100], "cb9d7a": [88, 95], "61": [88, 95, 97, 101, 105, 108], "94": [88, 95, 98, 100, 101, 105, 108], "9acca4": [88, 95], "48": [88, 95, 97, 98, 101, 105], "x_raw": [88, 95], "labels_raw": 88, "interg": [88, 89], "categorical_featur": [88, 108], "x_encod": [88, 95], "get_dummi": [88, 95, 108], "drop_first": [88, 95], "numeric_featur": [88, 95], "scaler": [88, 95, 106], "x_process": [88, 95], "fit_transform": [88, 95, 97, 100], "bring": [88, 89, 93, 95, 96, 103, 108], "byod": [88, 89, 93, 95, 96, 103, 108], "tress": 88, "held": [88, 90, 95, 96, 98, 105, 106, 107], "straightforward": [88, 90, 95], "benefit": [88, 90, 107, 109], "num_crossval_fold": [88, 90, 95, 100, 103], "tabl": [88, 95, 98, 103], "212": [88, 100, 101], "iloc": [88, 89, 90, 95, 96, 100, 108], "92": [88, 91, 100, 101, 105], "93": [88, 98, 100, 105, 108, 110], "827": 88, "99": [88, 97, 98, 100, 101], "86": [88, 92, 93, 95, 100, 101, 105, 108], "74": [88, 97, 100, 105, 108], "637": [88, 95], "79": [88, 98, 100, 105], "65": [88, 91, 97, 100, 105], "cheat": [88, 100], "0pt": [88, 100], "120": [88, 91, 92, 100], "233": 88, "83": [88, 100, 101, 105, 108, 110], "76": [88, 100, 101, 104, 105, 108], "suspici": [88, 95], "carefulli": [88, 93, 95, 96, 100], "examin": [88, 91, 92, 95, 97, 100, 105], "labels_train": 88, "labels_test": 88, "test_siz": [88, 89, 91, 92], "acc_og": [88, 89], "783068783068783": 88, "robustli": [88, 89, 108], "14": [88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "acc_cl": [88, 89], "8095238095238095": 88, "blindli": [88, 89, 90, 99, 100, 108], "trust": [88, 89, 90, 99, 100, 101, 103, 107, 108], "effort": [88, 89, 100, 108], "cumbersom": [88, 89, 92, 95, 96, 98, 101, 104, 106, 108], "intent": [89, 96], "servic": [89, 96, 99], "onlin": [89, 96], "bank": [89, 96, 98], "banking77": [89, 96], "oo": [89, 96], "categori": [89, 93, 96, 97, 100], "shortlist": [89, 96, 108], "scope": [89, 96], "logist": [89, 91, 92, 96, 103, 106], "probabilit": [89, 90], "drop": [89, 95, 97, 99, 100, 103, 108], "sentence_transform": [89, 96], "sentencetransform": [89, 96], "payment": [89, 96], "cancel_transf": [89, 96], "transfer": [89, 96], "fund": [89, 96], "cancel": [89, 96], "transact": [89, 96], "my": [89, 96], "revert": [89, 96], "morn": [89, 96], "realis": [89, 96], "yesterdai": [89, 96], "rent": [89, 96], "tomorrow": [89, 96], "raw_text": [89, 96], "raw_label": 89, "raw_train_text": 89, "raw_test_text": 89, "raw_train_label": 89, "raw_test_label": 89, "getting_spare_card": [89, 96], "card_payment_fee_charg": [89, 96], "supported_cards_and_curr": [89, 96], "beneficiary_not_allow": [89, 96], "apple_pay_or_google_pai": [89, 96], "change_pin": [89, 96], "visa_or_mastercard": [89, 96], "lost_or_stolen_phon": [89, 96], "card_about_to_expir": [89, 96], "card": [89, 96, 98], "utter": [89, 96], "encond": 89, "test_label": [89, 100, 101, 104, 106], "suit": [89, 96, 97, 98, 99], "electra": [89, 96], "discrimin": [89, 96], "googl": [89, 96], "train_text": 89, "test_text": 89, "home": [89, 96, 98], "runner": [89, 96], "google_electra": [89, 96], "pool": [89, 96, 99, 106], "leverag": [89, 90, 96, 99, 101, 103], "computation": [89, 90, 96], "intens": [89, 90, 96], "400": [89, 96, 100], "858371": 89, "547274": 89, "826228": 89, "966008": 89, "792449": 89, "identified_issu": [89, 108], "lowest_quality_label": [89, 90, 96, 101, 108], "to_numpi": [89, 96, 97, 100, 108], "44": [89, 97, 98, 104, 105], "646": 89, "390": 89, "628": 89, "121": [89, 101], "702": 89, "863": 89, "135": 89, "337": [89, 100, 105], "735": 89, "print_as_df": 89, "inverse_transform": 89, "charg": [89, 96], "cash": [89, 96], "holidai": [89, 96], "sent": [89, 96, 97, 110], "mine": [89, 96], "expir": [89, 96], "fight": 89, "hors": [89, 98, 106], "duck": [89, 98], "me": [89, 96, 97], "whoever": [89, 96], "consum": [89, 108], "18": [89, 90, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "baseline_model": [89, 108], "87": [89, 92, 93, 100, 105, 108], "acceler": [89, 108], "19": [89, 90, 93, 96, 97, 98, 99, 100, 101, 105, 106, 108, 109], "89": [89, 91, 95, 100, 105, 108], "spoken": 90, "500": [90, 97, 100, 106, 110], "english": [90, 98], "pronunci": 90, "wav": 90, "voxceleb": 90, "speech": [90, 110], "your_pred_prob": [90, 91, 92, 95, 96], "tensorflow_io": 90, "huggingface_hub": 90, "reproduc": [90, 95, 97, 100, 101, 103], "command": 90, "wget": [90, 97, 105, 109, 110], "navig": 90, "browser": 90, "jakobovski": 90, "archiv": [90, 110], "v1": 90, "tar": [90, 106], "gz": [90, 106], "mkdir": [90, 110], "spoken_digit": 90, "xf": 90, "6_nicolas_32": 90, "data_path": 90, "listdir": 90, "nondeterminist": 90, "file_nam": 90, "endswith": 90, "file_path": 90, "join": [90, 93, 97, 99, 100], "7_george_26": 90, "0_nicolas_24": 90, "0_nicolas_6": 90, "listen": 90, "display_exampl": 90, "expand": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "pulldown": [90, 91, 92, 93, 98, 100, 101, 103, 104, 106, 108, 110], "colab": [90, 91, 92, 93, 98, 99, 100, 101, 103, 104, 106, 108, 110], "tfio": 90, "pathlib": 90, "ipython": [90, 97], "load_wav_16k_mono": 90, "filenam": 90, "khz": 90, "file_cont": 90, "read_fil": 90, "sample_r": 90, "decode_wav": 90, "desired_channel": 90, "squeez": 90, "rate_in": 90, "rate_out": 90, "16000": 90, "wav_file_nam": 90, "audio_r": 90, "wav_file_exampl": 90, "plai": [90, 98, 99], "button": 90, "wav_file_name_exampl": 90, "7_jackson_43": 90, "hear": 90, "extractor": 90, "encoderclassifi": 90, "spkrec": 90, "xvect": 90, "feature_extractor": 90, "from_hparam": 90, "run_opt": 90, "uncom": [90, 97], "ffmpeg": 90, "backend": 90, "wav_audio_file_path": 90, "torchaudio": 90, "extract_audio_embed": 90, "emb": [90, 93], "signal": 90, "encode_batch": 90, "embeddings_list": [90, 93], "embeddings_arrai": 90, "512": [90, 93], "196311": 90, "319459": 90, "478975": 90, "2890875": 90, "8170238": 90, "89265": 90, "898056": 90, "256195": 90, "559641": 90, "559721": 90, "62067": 90, "285245": 90, "21": [90, 91, 97, 98, 100, 101, 105, 108, 110], "709627": 90, "5033693": 90, "913803": 90, "819831": 90, "1831515": 90, "208763": 90, "084257": 90, "3210397": 90, "005453": 90, "216152": 90, "478235": 90, "6821785": 90, "053807": 90, "242471": 90, "091424": 90, "78334856": 90, "03954": 90, "23": [90, 93, 97, 98, 100, 101, 105, 108], "569176": 90, "761097": 90, "1258295": 90, "753237": 90, "3508866": 90, "598274": 90, "23712": 90, "2500": 90, "tol": 90, "decreas": [90, 99], "cv_accuraci": 90, "9708": 90, "issue_type_descript": [90, 91, 92, 93, 95, 96, 100, 101], "lt": [90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 103, 106], "gt": [90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 110], "9976": 90, "986": 90, "002161": 90, "176": [90, 98, 101, 104], "002483": 90, "2318": 90, "004411": 90, "1005": 90, "004857": 90, "1871": 90, "007494": 90, "040587": 90, "999207": 90, "999377": 90, "975220": 90, "999367": 90, "identified_label_issu": [90, 96], "516": [90, 100], "1946": 90, "469": 90, "2132": 90, "worth": [90, 101], "6_yweweler_25": 90, "7_nicolas_43": 90, "6_theo_27": 90, "6_yweweler_36": 90, "6_yweweler_14": 90, "6_yweweler_35": 90, "6_nicolas_8": 90, "sound": 90, "quit": [90, 106], "underneath": 91, "hood": [91, 97, 99], "alert": 91, "introduct": 91, "mayb": [91, 92, 96], "your_feature_matrix": [91, 92], "toi": [91, 92, 93, 97, 98, 101, 103, 107], "inf": [91, 92], "mid": [91, 92], "bins_map": [91, 92], "create_data": [91, 92], "y_bin": [91, 92], "y_i": [91, 92], "y_bin_idx": [91, 92], "y_train": [91, 92, 101, 108], "y_test": [91, 92, 101, 108], "y_train_idx": [91, 92], "y_test_idx": [91, 92], "slide": [91, 92, 98], "frame": [91, 92], "x_out": [91, 92], "tini": [91, 92], "concaten": [91, 92, 107], "y_out": [91, 92], "y_out_bin": [91, 92], "y_out_bin_idx": [91, 92], "exact_duplicate_idx": [91, 92], "x_duplic": [91, 92], "y_duplic": [91, 92], "y_duplicate_idx": [91, 92], "noisy_labels_idx": [91, 92, 104], "scatter": [91, 92, 97, 101, 104, 108], "black": [91, 92, 98, 108], "cyan": [91, 92], "plot_data": [91, 92, 97, 101, 104, 108], "fig": [91, 92, 93, 98, 106, 108], "ax": [91, 92, 93, 97, 106, 108], "subplot": [91, 92, 93, 106], "set_titl": [91, 92, 93, 106], "set_xlabel": [91, 92], "x_1": [91, 92], "fontsiz": [91, 92, 93, 97, 101, 104], "set_ylabel": [91, 92], "x_2": [91, 92], "set_xlim": [91, 92], "set_ylim": [91, 92], "linestyl": [91, 92, 97], "circl": [91, 92, 101, 104], "misclassifi": [91, 92], "zip": [91, 92, 93, 97, 105, 110], "label_err": [91, 92], "180": [91, 92, 97, 105], "marker": [91, 92], "facecolor": [91, 92, 97], "edgecolor": [91, 92, 97], "linewidth": [91, 92, 97, 106], "dup": [91, 92], "first_legend": [91, 92], "align": [91, 92], "title_fontproperti": [91, 92], "semibold": [91, 92], "second_legend": [91, 92], "45": [91, 92, 97, 98, 100, 101, 105], "gca": [91, 92], "add_artist": [91, 92], "tight_layout": [91, 92, 97], "ideal": [91, 92], "remaind": 91, "modal": [91, 92, 99, 100, 103], "132": [91, 92, 100, 101, 105], "9318": 91, "006940": 91, "007830": 91, "40": [91, 92, 96, 97, 98, 100], "014828": 91, "107": [91, 92, 97, 101, 104], "021241": 91, "026407": 91, "notic": [91, 101, 103, 105], "3558": [91, 92], "126": [91, 92, 101, 105], "006636": [91, 92], "130": [91, 92], "012571": [91, 92], "129": [91, 92], "127": [91, 92, 100], "014909": [91, 92], "128": [91, 92, 93], "017443": [91, 92], "6160": [91, 92], "131": [91, 92, 100, 109], "000000e": [91, 92, 100], "000002": [91, 92], "463180e": [91, 92], "07": [91, 92, 93, 95, 97, 101, 105, 108], "51": [91, 92, 95, 97, 98, 101, 105], "161148": [91, 92], "859087e": [91, 92], "30": [91, 92, 93, 97, 98, 99, 100, 104, 109, 110], "3453": 91, "029542": 91, "031182": 91, "057961": 91, "058244": 91, "54": [91, 97, 98, 101, 105], "039122": 91, "044598": 91, "105": [91, 105], "105196": 91, "133654": 91, "43": [91, 97, 98, 100, 101, 105], "168033": 91, "125": 91, "101107": 91, "183382": 91, "109": [91, 97, 98, 100, 105], "209259": 91, "211042": 91, "221316": 91, "average_ood_scor": 91, "34530442089193386": 91, "52": [91, 97, 98, 100, 105, 110], "169820": 91, "087324e": 91, "259024": 91, "583757e": 91, "91": [91, 100, 105], "346458": 91, "341292e": 91, "specfi": 91, "new_lab": 91, "scoring_funct": 91, "div": 91, "rem": 91, "inv_scal": 91, "49": [91, 97, 98, 101, 105], "superstitionissuemanag": 91, "unlucki": 91, "superstit": 91, "to_seri": 91, "issues_mask": 91, "summary_scor": 91, "9242": 91, "is_superstition_issu": 91, "superstition_scor": 91, "26": [91, 93, 97, 98, 100, 101, 103, 105, 110], "047581": 91, "090635": 91, "129591": 91, "164840": 91, "lurk": [92, 93, 100, 101], "thoroughli": 92, "8561": 92, "001908": 92, "003564": 92, "007331": 92, "008963": 92, "009664": 92, "0227": 92, "022727": 92, "conceptu": 92, "856061": 92, "355772": 92, "616034": 92, "821750": 92, "926818": 92, "betweeen": 92, "859131": 92, "417707": 92, "664083": 92, "970324": 92, "816953": 92, "375317": 92, "641516": 92, "890575": 92, "910232": 92, "531021": 92, "460593": 92, "601188": 92, "826147": 92, "752808": 92, "321635": 92, "562539": 92, "948362": 92, "890169": 92, "090243": 92, "472909": 92, "746763": 92, "878267": 92, "examples_w_issu": [92, 99], "013445": 92, "025184": 92, "026376": 92, "inde": [92, 96], "miscellan": [92, 94, 110], "428571": 92, "111111": 92, "571429": 92, "407407": 92, "592593": 92, "337838": 92, "092593": 92, "662162": 92, "333333": [92, 98], "952381": 92, "666667": [92, 97], "portion": 92, "huge": [92, 101], "worri": [92, 96, 100], "critic": [92, 107], "60": [93, 97, 101, 108], "torchvis": [93, 97, 106], "tensordataset": 93, "stratifiedkfold": [93, 104], "tqdm": 93, "autonotebook": 93, "math": [93, 100], "fashion_mnist": 93, "num_row": 93, "60000": 93, "transformed_dataset": 93, "with_format": 93, "255": [93, 98], "cpu_count": 93, "torch_dataset": 93, "quick": [93, 104, 106], "super": 93, "relu": 93, "batchnorm2d": 93, "maxpool2d": 93, "lazylinear": 93, "flatten": 93, "get_test_accuraci": 93, "testload": [93, 106], "energi": 93, "trainload": [93, 106], "n_epoch": 93, "patienc": 93, "criterion": 93, "crossentropyloss": 93, "adamw": 93, "best_test_accuraci": 93, "start_epoch": 93, "running_loss": 93, "best_epoch": 93, "end_epoch": 93, "3f": [93, 108], "acc": [93, 101], "time_taken": 93, "compute_embed": 93, "compute_pred_prob": 93, "train_batch_s": 93, "num_work": 93, "worker": [93, 110], "train_id_list": 93, "test_id_list": 93, "train_id": 93, "test_id": 93, "embeddings_model": 93, "ntrain": 93, "trainset": 93, "testset": 93, "pin_memori": 93, "fold_embed": 93, "fold_pred_prob": 93, "finish": 93, "482": 93, "720": 93, "965": [93, 98], "329": [93, 95, 100, 105], "88": [93, 98, 100, 101, 104, 105, 108], "195": [93, 97, 100], "763": 93, "493": 93, "060": 93, "062": 93, "330": [93, 100, 105], "505": 93, "901": 93, "476": [93, 100], "340": [93, 100], "009": [93, 97], "328": [93, 105], "310": 93, "800": 93, "reorder": 93, "hstack": [93, 99, 101, 103], "max_preval": [93, 97], "7714": 93, "3772": 93, "3585": 93, "166": 93, "3651": 93, "27080": 93, "873833e": 93, "40378": 93, "915575e": 93, "25316": 93, "390277e": 93, "06": [93, 100, 101, 105, 110], "2090": 93, "751164e": 93, "14999": 93, "881301e": 93, "9569": 93, "11262": 93, "000003": 93, "coat": [93, 98], "shirt": [93, 98], "19228": 93, "000010": 93, "dress": 93, "32657": 93, "000013": 93, "bag": [93, 98, 106, 107], "21282": 93, "000016": [93, 100], "53564": 93, "000018": [93, 100], "pullov": 93, "6321": 93, "30968": 93, "001267": 93, "30659": 93, "000022": [93, 110], "47824": 93, "001454": 93, "3370": 93, "000026": 93, "54565": 93, "001854": 93, "9762": 93, "258": 93, "47139": 93, "000033": 93, "166980": 93, "986195": 93, "997205": 93, "sandal": [93, 98], "948781": 93, "999358": 93, "54078": 93, "17371": 93, "000025": 93, "plot_label_issue_exampl": 93, "nrow": [93, 106], "ceil": [93, 100], "axes_list": 93, "label_issue_indic": 93, "gl": 93, "sl": 93, "fontdict": 93, "imshow": [93, 106], "cmap": [93, 97, 108], "grai": 93, "subplots_adjust": 93, "hspace": 93, "outsiz": 93, "outlier_issu": [93, 96], "outlier_issues_df": 93, "depict": [93, 104, 105, 106, 107, 109], "plot_outlier_issues_exampl": 93, "n_comparison_imag": 93, "sample_from_class": 93, "number_of_sampl": 93, "non_outlier_indic": 93, "isnul": [93, 97], "non_outlier_indices_excluding_curr": 93, "sampled_indic": 93, "label_scores_of_sampl": 93, "top_score_indic": 93, "top_label_indic": 93, "sampled_imag": 93, "get_image_given_label_and_sampl": 93, "image_from_dataset": 93, "corresponding_label": 93, "comparison_imag": 93, "images_to_plot": 93, "idlist": 93, "iterrow": 93, "near_duplicate_issu": [93, 99], "closest": 93, "counterpart": 93, "near_duplicate_issues_df": 93, "plot_near_duplicate_issue_exampl": 93, "seen_id_pair": 93, "get_image_and_given_label_and_predicted_label": 93, "duplicate_imag": 93, "nd_set": 93, "challeng": 93, "dark_issu": 93, "reveal": [93, 105, 109], "dark_issues_df": 93, "is_dark_issu": [93, 97], "34848": 93, "203922": 93, "50270": 93, "204588": 93, "3936": 93, "213098": 93, "733": 93, "217686": 93, "8094": 93, "230118": 93, "plot_image_issue_exampl": 93, "difficult": 93, "disproportion": [93, 97], "lowinfo_issu": 93, "lowinfo_issues_df": 93, "is_low_information_issu": 93, "53050": 93, "067975": 93, "40875": 93, "089929": 93, "9594": 93, "092601": 93, "34825": 93, "107744": 93, "37530": 93, "108516": 93, "lot": 93, "workflow": [94, 99, 100, 102, 108], "histgradientboostingclassifi": 95, "cat_featur": 95, "boost": [95, 99, 103, 108], "xgboost": [95, 99, 100, 108], "think": [95, 96, 99, 104, 109, 110], "nonzero": 95, "358": 95, "941": 95, "294": [95, 105], "46": [95, 97, 98, 100, 101, 105], "7109": 95, "000005": [95, 96], "886": 95, "000059": 95, "709": [95, 100], "000104": 95, "000169": 95, "689": 95, "000181": 95, "3590": 95, "051882e": 95, "683133e": 95, "536582e": 95, "406589e": 95, "324246e": 95, "6165": 95, "582": [95, 100], "185": [95, 97, 98, 105, 110], "187": [95, 98, 100], "898": 95, "0000": [95, 96, 98, 100, 101], "865": 95, "515002": 95, "837": 95, "556480": 95, "622": 95, "593068": 95, "593207": 95, "920": 95, "618041": 95, "4386345844794593e": 95, "issue_result": 95, "000842": 95, "555944": 95, "004374": 95, "sorted_issu": 95, "73": [95, 97, 98, 100, 104, 105, 108], "deserv": 95, "outlier_result": 95, "sorted_outli": 95, "56": [95, 97, 98, 108], "96": [95, 97, 98, 100, 101, 104, 105, 108], "style": [95, 97, 109], "font": 95, "18px": 95, "ff00ff": 95, "bac": 95, "duplicate_result": 95, "lowest_scoring_dupl": 95, "idxmin": [95, 99], "indices_to_displai": 95, "tolist": [95, 99, 100, 104], "perhap": [95, 101, 103], "second_lowest_scoring_dupl": 95, "next_indices_to_displai": 95, "wari": [95, 96, 99], "your_featur": 96, "text_embed": 96, "data_dict": [96, 101, 103], "85": [96, 100, 105], "38": [96, 97, 98, 105], "9710": 96, "981": 96, "974": 96, "000146": 96, "982": [96, 98], "000224": 96, "971": 96, "000507": 96, "980": [96, 98], "000960": 96, "3584": 96, "994": 96, "009642": 96, "999": 96, "013067": 96, "013841": 96, "433": 96, "014722": 96, "989": 96, "018224": 96, "6070": 96, "160": [96, 108], "095724": 96, "148": 96, "006237": 96, "546": [96, 100], "099341": 96, "514": 96, "006485": 96, "481": 96, "123418": 96, "008165": 96, "313": [96, 100, 105], "564102": 96, "572258": 96, "574915": 96, "31": [96, 97, 98, 100, 101, 103, 105], "575507": 96, "575874": 96, "792090": 96, "257611": 96, "698710": 96, "182121": 96, "771619": 96, "data_with_suggested_label": 96, "suggested_label": 96, "withdraw": 96, "monei": 96, "lowest_quality_outli": 96, "OR": 96, "636c65616e6c616220697320617765736f6d6521": 96, "phone": [96, 98], "gone": 96, "samp": 96, "br": 96, "press": [96, 110], "nonsens": 96, "sens": 96, "detriment": 96, "duplicate_issu": 96, "fee": 96, "go": [96, 97, 98, 101], "p_valu": 96, "benign": 96, "curat": [96, 102], "bigger": 97, "make_classif": 97, "5000": [97, 106], "n_featur": 97, "n_inform": 97, "n_redund": 97, "n_repeat": 97, "n_class": 97, "n_clusters_per_class": 97, "flip_i": 97, "class_sep": 97, "faiss": 97, "x_faiss": 97, "float32": [97, 105], "normalize_l2": 97, "index_factori": 97, "hnsw32": 97, "flat": [97, 98], "metric_inner_product": 97, "a_min": 97, "a_max": 97, "create_knn_graph": 97, "assert": 97, "indices_1d": 97, "ravel": 97, "distances_1d": 97, "sort_graph_by_row_valu": 97, "warn_when_not_sort": 97, "50000": 97, "524": 97, "991400": 97, "356924": 97, "363": [97, 100], "619581": 97, "108": [97, 105], "500000": 97, "651838": 97, "999827": 97, "031217": 97, "933716": 97, "627345": 97, "998540": 97, "530909": 97, "296974": 97, "646765": 97, "942721": 97, "332824": 97, "803246": 97, "625202": 97, "999816": 97, "474031": 97, "706253": 97, "655108": 97, "997703": 97, "131466": 97, "912389": 97, "639200": 97, "4995": 97, "998646": 97, "504755": 97, "746777": 97, "680033": 97, "4996": 97, "894230": 97, "340986": 97, "816472": 97, "640711": 97, "4997": 97, "999100": 97, "428545": 97, "592421": 97, "658949": 97, "4998": 97, "986792": 97, "273710": 97, "618033": 97, "4999": 97, "986776": 97, "273524": 97, "618084": 97, "instabl": 97, "proxim": 97, "analys": 97, "comfort": 97, "explor": [97, 105, 106], "third": 97, "parti": [97, 110], "newsgroup": 97, "alt": [97, 98], "atheism": [97, 98], "sci": [97, 98], "fetch_20newsgroup": 97, "newsgroups_train": 97, "header": 97, "footer": 97, "quot": 97, "df_text": 97, "target_nam": 97, "enlighten": 97, "omnipot": 97, "19apr199320262420": 97, "kelvin": 97, "jpl": 97, "nasa": 97, "gov": 97, "baa": 97, "nhenri": 97, "he": 97, "nno": 97, "ge": 97, "nlucki": 97, "babi": [97, 98], "tfidfvector": 97, "feature_extract": 97, "x_vector": 97, "data_valuation_issu": 97, "147": [97, 101, 105], "500047": 97, "500093": 97, "499953": 97, "1068": 97, "1069": 97, "1070": 97, "1071": 97, "1072": 97, "1073": 97, "concentr": 97, "seaborn": 97, "sn": 97, "distinguish": [97, 100], "strip": 97, "stripplot": 97, "hue": [97, 108], "dodg": 97, "jitter": 97, "axvlin": [97, 106], "xlabel": 97, "ourselv": 97, "make_blob": 97, "center": [97, 98], "cluster_std": 97, "n_noisy_label": 97, "meaning": [97, 99, 100, 106], "silhouette_scor": 97, "gridsearchcv": 97, "silhouett": 97, "cluster_label": 97, "fit_predict": 97, "param_grid": [97, 100], "grid_search": 97, "best_kmean": 97, "best_estimator_": 97, "underperforming_group_issu": 97, "328308": 97, "tab10": 97, "domain": 97, "knowledg": [97, 101], "dataset_tsv": 97, "ag": [97, 108], "gender": 97, "educ": 97, "experi": 97, "highsalari": 97, "indiana": 97, "phd": 97, "male": 97, "bachelor": 97, "femal": 97, "kansa": 97, "school": [97, 98], "ohio": 97, "57": [97, 98, 100, 101, 110], "california": 97, "59": [97, 98, 105], "34": [97, 98, 101, 103, 105, 110], "63": [97, 100, 101, 105, 108], "47": [97, 98, 105], "stringio": 97, "sep": [97, 110], "easier": [97, 101], "simplic": [97, 104], "ordinalencod": 97, "columns_to_encod": 97, "encoded_df": 97, "salari": 97, "573681": 97, "underpin": 97, "caught": 97, "whenev": 97, "generate_data_depend": 97, "num_sampl": 97, "a1": 97, "a2": 97, "a3": 97, "375": 97, "975": 97, "non_iid_issu": 97, "796474": 97, "842432": 97, "922562": 97, "820759": 97, "873136": 97, "887373": 97, "825101": 97, "855875": 97, "751795": 97, "835796": 97, "ylabel": [97, 106], "coolwarm": 97, "colorbar": [97, 108], "strong": 97, "evid": [97, 100], "inter": 97, "mitig": 97, "risk": [97, 100], "deeper": 97, "tsv": 97, "tab": 97, "pars": 97, "annual_spend": 97, "number_of_transact": 97, "last_purchase_d": 97, "rural": 97, "4099": 97, "2024": [97, 110], "6421": 97, "nat": 97, "suburban": 97, "5436": 97, "4046": 97, "66": [97, 98, 100], "3467": 97, "67": [97, 98, 100, 105, 108, 110], "4757": 97, "4199": 97, "4991": 97, "4655": 97, "82": [97, 98, 100, 101, 105, 108], "5584": 97, "urban": 97, "3102": 97, "6637": 97, "9167": 97, "6790": 97, "5327": 97, "parse_d": 97, "lose": 97, "intact": 97, "encode_categorical_column": 97, "placehold": 97, "dropna": [97, 103], "category_to_numb": 97, "_encod": 97, "gender_encod": 97, "location_encod": 97, "focus": [97, 100, 101, 103, 104, 108], "null_issu": 97, "833333": 97, "sorted_indic": [97, 105], "sorted_df": 97, "nice": 97, "styler": 97, "combined_df": 97, "concat": [97, 100, 108], "highlight_null_valu": 97, "val": [97, 101], "yellow": [97, 98], "highlight_datalab_column": 97, "lightblu": 97, "highlight_is_null_issu": 97, "orang": [97, 98], "styled_df": 97, "nbsp": [97, 99, 100, 101], "160000": 97, "820000": 97, "460000": 97, "470000": 97, "960000": 97, "620000": 97, "550000": 97, "660000": 97, "670000": [97, 98], "370000": 97, "530000": 97, "710000": 97, "020000": 97, "320000": 97, "990000": 97, "rarer": 97, "fairer": 97, "randomli": [97, 100, 101], "class_imbalance_issu": 97, "countplot": 97, "xtick": 97, "rotat": 97, "ytick": 97, "filtered_df": 97, "xy": 97, "va": 97, "textual": 97, "get_ytick": 97, "nbar": 97, "nimbal": 97, "get_legend_handles_label": 97, "title_fonts": 97, "aspect": 97, "anomali": [97, 105], "enhanc": [97, 101, 103, 105], "artifici": 97, "directori": [97, 110], "subdirectori": 97, "nc": [97, 105, 109, 110], "unzip": [97, 105, 110], "09": [97, 100, 104, 105, 108, 110], "199": [97, 100, 105], "111": [97, 103, 108], "153": [97, 100, 105], "110": [97, 105], "connect": [97, 110], "443": [97, 110], "await": [97, 110], "ok": [97, 107, 110], "986707": 97, "964k": 97, "963": 97, "58k": 97, "kb": [97, 110], "mb": [97, 110], "imagefold": 97, "load_image_dataset": 97, "data_dir": 97, "root": [97, 106], "image_dataset": 97, "img": [97, 106, 108], "from_dict": [97, 99], "darkened_imag": 97, "job": 97, "015": 97, "label_uncorrelatedness_scor": 97, "image_issu": 97, "nimag": 97, "237196": 97, "197229": 97, "254188": 97, "229170": 97, "208907": 97, "793840": 97, "196": [97, 100, 101, 105], "197": [97, 101, 105], "971560": 97, "198": [97, 101, 105], "862236": 97, "973533": 97, "stronger": 97, "frog": [97, 98, 106], "darken": 97, "concept": 97, "notabl": 97, "preval": 97, "warrant": 97, "programmat": 97, "plot_scores_label": 97, "issues_copi": 97, "boxplot": 97, "refin": 98, "instruct": [98, 99, 100], "studi": [98, 105], "mnist_test_set": 98, "imagenet_val_set": 98, "tench": 98, "goldfish": 98, "white": [98, 110], "shark": 98, "tiger": 98, "hammerhead": 98, "electr": 98, "rai": 98, "stingrai": 98, "cock": 98, "hen": 98, "ostrich": 98, "brambl": 98, "goldfinch": 98, "hous": 98, "finch": 98, "junco": 98, "indigo": 98, "bunt": 98, "american": [98, 110], "robin": 98, "bulbul": 98, "jai": 98, "magpi": 98, "chickade": 98, "dipper": 98, "kite": 98, "bald": 98, "eagl": 98, "vultur": 98, "grei": 98, "owl": 98, "salamand": 98, "smooth": 98, "newt": 98, "spot": [98, 99, 105], "axolotl": 98, "bullfrog": 98, "tree": 98, "tail": 98, "loggerhead": 98, "sea": 98, "turtl": 98, "leatherback": 98, "mud": 98, "terrapin": 98, "band": 98, "gecko": 98, "green": [98, 110], "iguana": 98, "carolina": 98, "anol": 98, "desert": 98, "grassland": 98, "whiptail": 98, "lizard": 98, "agama": 98, "frill": 98, "neck": 98, "allig": 98, "gila": 98, "monster": 98, "european": 98, "chameleon": 98, "komodo": 98, "dragon": 98, "nile": 98, "crocodil": 98, "triceratop": 98, "worm": 98, "snake": 98, "ring": 98, "eastern": 98, "hog": 98, "nose": 98, "kingsnak": 98, "garter": 98, "water": 98, "vine": 98, "night": 98, "boa": 98, "constrictor": 98, "african": 98, "rock": 98, "indian": 98, "cobra": 98, "mamba": 98, "saharan": 98, "horn": 98, "viper": 98, "diamondback": 98, "rattlesnak": 98, "sidewind": 98, "trilobit": 98, "harvestman": 98, "scorpion": 98, "garden": 98, "spider": 98, "barn": 98, "southern": 98, "widow": 98, "tarantula": 98, "wolf": 98, "tick": 98, "centiped": 98, "grous": 98, "ptarmigan": 98, "ruf": 98, "prairi": 98, "peacock": 98, "quail": 98, "partridg": 98, "parrot": 98, "macaw": 98, "sulphur": 98, "crest": 98, "cockatoo": 98, "lorikeet": 98, "coucal": 98, "bee": 98, "eater": 98, "hornbil": 98, "hummingbird": 98, "jacamar": 98, "toucan": 98, "breast": 98, "mergans": 98, "goos": 98, "swan": 98, "tusker": 98, "echidna": 98, "platypu": 98, "wallabi": 98, "koala": 98, "wombat": 98, "jellyfish": 98, "anemon": 98, "brain": 98, "coral": 98, "flatworm": 98, "nematod": 98, "conch": 98, "snail": 98, "slug": 98, "chiton": 98, "chamber": 98, "nautilu": 98, "dung": 98, "crab": 98, "fiddler": 98, "king": 98, "lobster": 98, "spini": 98, "crayfish": 98, "hermit": 98, "isopod": 98, "stork": 98, "spoonbil": 98, "flamingo": 98, "heron": 98, "egret": 98, "bittern": 98, "crane": 98, "bird": [98, 106], "limpkin": 98, "gallinul": 98, "coot": 98, "bustard": 98, "ruddi": 98, "turnston": 98, "dunlin": 98, "redshank": 98, "dowitch": 98, "oystercatch": 98, "pelican": 98, "penguin": 98, "albatross": 98, "whale": 98, "killer": 98, "dugong": 98, "lion": 98, "chihuahua": 98, "japanes": 98, "chin": 98, "maltes": 98, "pekinges": 98, "shih": 98, "tzu": 98, "charl": 98, "spaniel": 98, "papillon": 98, "terrier": 98, "rhodesian": 98, "ridgeback": 98, "afghan": [98, 110], "hound": 98, "basset": 98, "beagl": 98, "bloodhound": 98, "bluetick": 98, "coonhound": 98, "tan": 98, "walker": 98, "foxhound": 98, "redbon": 98, "borzoi": 98, "irish": 98, "wolfhound": 98, "italian": 98, "greyhound": 98, "whippet": 98, "ibizan": 98, "norwegian": 98, "elkhound": 98, "otterhound": 98, "saluki": 98, "scottish": 98, "deerhound": 98, "weimaran": 98, "staffordshir": 98, "bull": 98, "bedlington": 98, "border": 98, "kerri": 98, "norfolk": 98, "norwich": 98, "yorkshir": 98, "wire": 98, "fox": 98, "lakeland": 98, "sealyham": 98, "airedal": 98, "cairn": 98, "australian": 98, "dandi": 98, "dinmont": 98, "boston": 98, "miniatur": 98, "schnauzer": 98, "giant": 98, "tibetan": 98, "silki": 98, "wheaten": 98, "west": 98, "highland": 98, "lhasa": 98, "apso": 98, "retriev": 98, "curli": 98, "golden": 98, "labrador": 98, "chesapeak": 98, "bai": 98, "german": [98, 110], "shorthair": 98, "pointer": 98, "vizsla": 98, "setter": 98, "gordon": 98, "brittani": 98, "clumber": 98, "springer": 98, "welsh": 98, "cocker": 98, "sussex": 98, "kuvasz": 98, "schipperk": 98, "groenendael": 98, "malinoi": 98, "briard": 98, "kelpi": 98, "komondor": 98, "sheepdog": 98, "shetland": 98, "colli": 98, "bouvier": 98, "de": 98, "flandr": 98, "rottweil": 98, "shepherd": 98, "dobermann": 98, "pinscher": 98, "swiss": [98, 110], "mountain": 98, "bernes": 98, "appenzel": 98, "sennenhund": 98, "entlebuch": 98, "boxer": 98, "bullmastiff": 98, "mastiff": 98, "french": 98, "bulldog": 98, "dane": 98, "st": 98, "bernard": 98, "huski": 98, "alaskan": 98, "malamut": 98, "siberian": 98, "dalmatian": 98, "affenpinsch": 98, "basenji": 98, "pug": 98, "leonberg": 98, "newfoundland": 98, "pyrenean": 98, "samoi": 98, "pomeranian": 98, "chow": 98, "keeshond": 98, "griffon": 98, "bruxelloi": 98, "pembrok": 98, "corgi": 98, "cardigan": 98, "poodl": 98, "mexican": 98, "hairless": 98, "tundra": 98, "coyot": 98, "dingo": 98, "dhole": 98, "wild": 98, "hyena": 98, "kit": 98, "arctic": 98, "tabbi": 98, "persian": 98, "siames": 98, "egyptian": 98, "mau": 98, "cougar": 98, "lynx": 98, "leopard": 98, "snow": 98, "jaguar": 98, "cheetah": 98, "brown": [98, 109], "bear": 98, "polar": 98, "sloth": 98, "mongoos": 98, "meerkat": 98, "beetl": 98, "ladybug": 98, "longhorn": 98, "leaf": 98, "rhinocero": 98, "weevil": 98, "fly": 98, "ant": 98, "grasshopp": 98, "cricket": 98, "stick": 98, "insect": 98, "cockroach": 98, "manti": 98, "cicada": 98, "leafhopp": 98, "lacew": 98, "dragonfli": 98, "damselfli": 98, "admir": 98, "ringlet": 98, "monarch": 98, "butterfli": 98, "gossam": 98, "wing": 98, "starfish": 98, "urchin": 98, "cucumb": 98, "cottontail": 98, "rabbit": 98, "hare": 98, "angora": 98, "hamster": 98, "porcupin": 98, "squirrel": 98, "marmot": 98, "beaver": 98, "guinea": 98, "pig": 98, "sorrel": 98, "zebra": 98, "boar": 98, "warthog": 98, "hippopotamu": 98, "ox": 98, "buffalo": 98, "bison": 98, "bighorn": 98, "sheep": 98, "alpin": 98, "ibex": 98, "hartebeest": 98, "impala": 98, "gazel": 98, "dromedari": 98, "llama": 98, "weasel": 98, "mink": 98, "polecat": 98, "foot": 98, "ferret": 98, "otter": 98, "skunk": 98, "badger": 98, "armadillo": 98, "toed": 98, "orangutan": 98, "gorilla": 98, "chimpanze": 98, "gibbon": 98, "siamang": 98, "guenon": 98, "pata": 98, "monkei": 98, "baboon": 98, "macaqu": 98, "langur": 98, "colobu": 98, "probosci": 98, "marmoset": 98, "capuchin": 98, "howler": 98, "titi": 98, "geoffroi": 98, "lemur": 98, "indri": 98, "asian": 98, "eleph": 98, "bush": 98, "snoek": 98, "eel": 98, "coho": 98, "salmon": 98, "beauti": 98, "clownfish": 98, "sturgeon": 98, "garfish": 98, "lionfish": 98, "pufferfish": 98, "abacu": 98, "abaya": 98, "academ": 98, "gown": 98, "accordion": 98, "acoust": 98, "guitar": 98, "aircraft": 98, "carrier": 98, "airlin": 98, "airship": 98, "altar": 98, "ambul": 98, "amphibi": 98, "clock": [98, 110], "apiari": 98, "apron": 98, "wast": 98, "assault": 98, "rifl": 98, "backpack": 98, "bakeri": 98, "balanc": 98, "beam": 98, "balloon": 98, "ballpoint": 98, "pen": 98, "aid": 98, "banjo": 98, "balust": 98, "barbel": 98, "barber": 98, "chair": [98, 105], "barbershop": 98, "baromet": 98, "barrel": 98, "wheelbarrow": 98, "basebal": 98, "basketbal": 98, "bassinet": 98, "bassoon": 98, "swim": 98, "cap": 98, "bath": 98, "towel": 98, "bathtub": 98, "station": 98, "wagon": 98, "lighthous": 98, "beaker": 98, "militari": 98, "beer": 98, "bottl": 98, "glass": 98, "bell": 98, "cot": 98, "bib": 98, "bicycl": [98, 109], "bikini": 98, "binder": 98, "binocular": 98, "birdhous": 98, "boathous": 98, "bobsleigh": 98, "bolo": 98, "tie": 98, "poke": 98, "bonnet": 98, "bookcas": 98, "bookstor": 98, "bow": 98, "brass": 98, "bra": 98, "breakwat": 98, "breastplat": 98, "broom": 98, "bucket": 98, "buckl": 98, "bulletproof": 98, "vest": 98, "butcher": 98, "shop": 98, "taxicab": 98, "cauldron": 98, "candl": 98, "cannon": 98, "cano": 98, "mirror": [98, 105], "carousel": 98, "carton": 98, "wheel": 98, "teller": 98, "cassett": 98, "player": 98, "castl": 98, "catamaran": 98, "cd": 98, "cello": 98, "mobil": [98, 110], "chain": 98, "fenc": [98, 109], "mail": 98, "chainsaw": 98, "chest": 98, "chiffoni": 98, "chime": 98, "china": 98, "cabinet": 98, "christma": 98, "stock": 98, "church": 98, "movi": 98, "theater": 98, "cleaver": 98, "cliff": 98, "dwell": 98, "cloak": 98, "clog": 98, "cocktail": 98, "shaker": 98, "coffe": 98, "mug": 98, "coffeemak": 98, "coil": 98, "lock": 98, "keyboard": 98, "confectioneri": 98, "ship": [98, 106], "corkscrew": 98, "cornet": 98, "cowboi": 98, "boot": 98, "hat": 98, "cradl": 98, "crash": 98, "helmet": 98, "crate": 98, "infant": 98, "bed": 98, "crock": 98, "pot": 98, "croquet": 98, "crutch": 98, "cuirass": 98, "dam": 98, "desk": 98, "desktop": 98, "rotari": 98, "dial": 98, "telephon": 98, "diaper": 98, "watch": 98, "dine": 98, "dishcloth": 98, "dishwash": 98, "disc": 98, "brake": 98, "dock": 98, "sled": 98, "dome": 98, "doormat": 98, "drill": 98, "rig": 98, "drum": 98, "drumstick": 98, "dumbbel": 98, "dutch": 98, "oven": 98, "fan": 98, "locomot": 98, "entertain": 98, "envelop": 98, "espresso": 98, "powder": 98, "feather": 98, "fireboat": 98, "engin": [98, 109], "screen": 98, "sheet": 98, "flagpol": 98, "flute": 98, "footbal": 98, "forklift": 98, "fountain": 98, "poster": 98, "freight": 98, "fry": 98, "pan": 98, "fur": 98, "garbag": 98, "ga": 98, "pump": 98, "goblet": 98, "kart": 98, "golf": 98, "cart": 98, "gondola": 98, "gong": 98, "grand": 98, "piano": 98, "greenhous": 98, "grill": 98, "groceri": 98, "guillotin": 98, "barrett": 98, "hair": 98, "sprai": 98, "hammer": 98, "dryer": 98, "hand": [98, 101], "handkerchief": 98, "drive": 98, "harmonica": 98, "harp": 98, "harvest": 98, "hatchet": 98, "holster": 98, "honeycomb": 98, "hoop": 98, "skirt": 98, "horizont": 98, "bar": 98, "drawn": 98, "hourglass": 98, "ipod": 98, "cloth": 98, "iron": 98, "jack": 98, "lantern": 98, "jean": 98, "jeep": 98, "jigsaw": 98, "puzzl": 98, "pull": 98, "rickshaw": 98, "joystick": 98, "kimono": 98, "knee": 98, "pad": 98, "knot": 98, "ladl": 98, "lampshad": 98, "laptop": 98, "lawn": 98, "mower": 98, "knife": 98, "lifeboat": 98, "lighter": 98, "limousin": 98, "ocean": 98, "liner": 98, "lipstick": 98, "slip": 98, "shoe": 98, "lotion": 98, "speaker": 98, "loup": 98, "sawmil": 98, "magnet": 98, "compass": 98, "mailbox": 98, "tight": 98, "tank": 98, "manhol": 98, "maraca": 98, "marimba": 98, "maypol": 98, "maze": 98, "cup": [98, 105], "medicin": 98, "megalith": 98, "microphon": 98, "microwav": 98, "milk": 98, "minibu": 98, "miniskirt": 98, "minivan": 98, "missil": 98, "mitten": [98, 99], "mix": 98, "bowl": 98, "modem": 98, "monasteri": 98, "monitor": 98, "mope": 98, "mortar": 98, "mosqu": 98, "mosquito": 98, "scooter": 98, "bike": 98, "tent": 98, "mous": [98, 99], "mousetrap": 98, "van": 98, "muzzl": 98, "nail": 98, "brace": 98, "necklac": 98, "nippl": 98, "obelisk": 98, "obo": 98, "ocarina": 98, "odomet": 98, "oil": 98, "oscilloscop": 98, "overskirt": 98, "bullock": 98, "oxygen": 98, "packet": 98, "paddl": 98, "padlock": 98, "paintbrush": 98, "pajama": 98, "palac": [98, 110], "parachut": 98, "park": 98, "bench": 98, "meter": 98, "passeng": 98, "patio": 98, "payphon": 98, "pedest": 98, "pencil": 98, "perfum": 98, "petri": 98, "dish": 98, "photocopi": 98, "plectrum": 98, "pickelhaub": 98, "picket": 98, "pickup": 98, "pier": 98, "piggi": 98, "pill": 98, "pillow": 98, "ping": 98, "pong": 98, "pinwheel": 98, "pirat": 98, "pitcher": 98, "plane": 98, "planetarium": 98, "plastic": 98, "plate": 98, "rack": 98, "plow": 98, "plunger": 98, "polaroid": 98, "camera": 98, "pole": [98, 109], "polic": 98, "poncho": 98, "billiard": 98, "soda": 98, "potter": 98, "prayer": 98, "rug": 98, "printer": 98, "prison": 98, "projectil": 98, "projector": 98, "hockei": 98, "puck": 98, "punch": 98, "purs": 98, "quill": 98, "quilt": 98, "race": 98, "racket": 98, "radiat": 98, "radio": 98, "telescop": 98, "rain": 98, "recreat": 98, "reel": 98, "reflex": 98, "refriger": 98, "remot": 98, "restaur": 98, "revolv": 98, "rotisseri": 98, "eras": 98, "rugbi": 98, "ruler": 98, "safe": 98, "safeti": 98, "salt": 98, "sarong": 98, "saxophon": 98, "scabbard": 98, "bu": [98, 109], "schooner": 98, "scoreboard": 98, "crt": 98, "screw": 98, "screwdriv": 98, "seat": 98, "belt": 98, "sew": 98, "shield": 98, "shoji": 98, "basket": 98, "shovel": 98, "shower": 98, "curtain": 98, "ski": 98, "sleep": 98, "door": 98, "slot": 98, "snorkel": 98, "snowmobil": 98, "snowplow": 98, "soap": 98, "dispens": 98, "soccer": [98, 110], "sock": [98, 99], "solar": 98, "thermal": 98, "collector": 98, "sombrero": 98, "soup": 98, "heater": 98, "shuttl": 98, "spatula": 98, "motorboat": 98, "web": 98, "spindl": 98, "sport": [98, 110], "spotlight": 98, "stage": 98, "steam": 98, "arch": 98, "bridg": 98, "steel": 98, "stethoscop": 98, "scarf": 98, "stone": 98, "wall": [98, 109], "stopwatch": 98, "stove": 98, "strainer": 98, "tram": 98, "stretcher": 98, "couch": 98, "stupa": 98, "submarin": 98, "sundial": 98, "sunglass": 98, "sunscreen": 98, "suspens": 98, "mop": 98, "sweatshirt": 98, "swimsuit": 98, "swing": 98, "switch": 98, "syring": 98, "lamp": 98, "tape": 98, "teapot": 98, "teddi": 98, "televis": [98, 110], "tenni": 98, "thatch": 98, "roof": 98, "thimbl": 98, "thresh": 98, "throne": 98, "tile": 98, "toaster": 98, "tobacco": 98, "toilet": 98, "totem": 98, "tow": 98, "tractor": 98, "semi": 98, "trailer": 98, "trai": 98, "trench": 98, "tricycl": 98, "trimaran": 98, "tripod": 98, "triumphal": 98, "trolleybu": 98, "trombon": 98, "tub": 98, "turnstil": 98, "typewrit": 98, "umbrella": 98, "unicycl": 98, "upright": 98, "vacuum": 98, "cleaner": [98, 100], "vase": 98, "vault": 98, "velvet": 98, "vend": 98, "vestment": 98, "viaduct": 98, "violin": 98, "volleybal": 98, "waffl": 98, "wallet": 98, "wardrob": 98, "sink": 98, "wash": 98, "jug": 98, "tower": 98, "whiskei": 98, "whistl": 98, "wig": 98, "shade": [98, 109], "windsor": 98, "wine": 98, "wok": 98, "wooden": 98, "spoon": 98, "wool": 98, "rail": 98, "shipwreck": 98, "yawl": 98, "yurt": 98, "websit": 98, "comic": 98, "book": 98, "crossword": 98, "traffic": [98, 105, 109], "sign": [98, 109, 110], "dust": 98, "jacket": [98, 105], "menu": 98, "guacamol": 98, "consomm": 98, "trifl": 98, "ic": 98, "cream": 98, "pop": 98, "baguett": 98, "bagel": 98, "pretzel": 98, "cheeseburg": 98, "mash": 98, "potato": 98, "cabbag": 98, "broccoli": 98, "cauliflow": 98, "zucchini": 98, "spaghetti": 98, "squash": 98, "acorn": 98, "butternut": 98, "artichok": 98, "pepper": [98, 99], "cardoon": 98, "mushroom": 98, "granni": 98, "smith": 98, "strawberri": 98, "lemon": 98, "pineappl": 98, "banana": 98, "jackfruit": 98, "custard": 98, "appl": 98, "pomegran": 98, "hai": 98, "carbonara": 98, "chocol": 98, "syrup": 98, "dough": 98, "meatloaf": 98, "pizza": 98, "pie": 98, "burrito": 98, "eggnog": 98, "alp": 98, "bubbl": 98, "reef": 98, "geyser": 98, "lakeshor": 98, "promontori": 98, "shoal": 98, "seashor": 98, "vallei": 98, "volcano": 98, "bridegroom": 98, "scuba": 98, "diver": 98, "rapese": 98, "daisi": 98, "ladi": 98, "slipper": 98, "corn": 98, "rose": 98, "hip": 98, "chestnut": 98, "fungu": 98, "agar": 98, "gyromitra": 98, "stinkhorn": 98, "earth": 98, "star": 98, "wood": 98, "bolet": 98, "ear": 98, "cifar10_test_set": 98, "airplan": [98, 106], "automobil": [98, 106], "deer": [98, 106], "cifar100_test_set": 98, "aquarium_fish": 98, "boi": 98, "camel": 98, "caterpillar": 98, "cattl": [98, 110], "cloud": 98, "dinosaur": 98, "dolphin": 98, "flatfish": 98, "forest": 98, "girl": 98, "kangaroo": 98, "lawn_mow": 98, "man": 98, "maple_tre": 98, "motorcycl": [98, 109], "oak_tre": 98, "orchid": 98, "palm_tre": 98, "pear": 98, "pickup_truck": 98, "pine_tre": 98, "plain": 98, "poppi": 98, "possum": 98, "raccoon": 98, "road": [98, 109], "rocket": 98, "seal": 98, "shrew": 98, "skyscrap": 98, "streetcar": 98, "sunflow": 98, "sweet_pepp": 98, "trout": 98, "tulip": 98, "willow_tre": 98, "woman": [98, 105], "caltech256": 98, "ak47": 98, "bat": 98, "glove": 98, "birdbath": 98, "blimp": 98, "bonsai": 98, "boom": 98, "breadmak": 98, "buddha": 98, "bulldoz": 98, "cactu": 98, "cake": 98, "tire": 98, "cartman": 98, "cereal": 98, "chandeli": 98, "chess": 98, "board": 98, "chimp": 98, "chopstick": 98, "coffin": 98, "coin": 98, "comet": 98, "cormor": 98, "globe": 98, "diamond": 98, "dice": 98, "doorknob": 98, "drink": 98, "straw": 98, "dumb": 98, "eiffel": 98, "elk": 98, "ewer": 98, "eyeglass": 98, "fern": 98, "fighter": 98, "jet": [98, 108], "extinguish": 98, "hydrant": 98, "firework": 98, "flashlight": 98, "floppi": 98, "fri": 98, "frisbe": 98, "galaxi": 98, "giraff": 98, "goat": 98, "gate": 98, "grape": 98, "pick": [98, 99], "hamburg": 98, "hammock": 98, "harpsichord": 98, "hawksbil": 98, "helicopt": 98, "hibiscu": 98, "homer": 98, "simpson": 98, "horsesho": 98, "air": 98, "skeleton": 98, "ibi": 98, "cone": 98, "iri": 98, "jesu": 98, "christ": 98, "joi": 98, "kayak": 98, "ketch": 98, "ladder": 98, "lath": 98, "licens": 98, "lightbulb": 98, "lightn": 98, "mandolin": 98, "mar": 98, "mattress": 98, "megaphon": 98, "menorah": 98, "microscop": 98, "minaret": 98, "minotaur": 98, "motorbik": 98, "mussel": 98, "neckti": 98, "octopu": 98, "palm": 98, "pilot": 98, "paperclip": 98, "shredder": 98, "pci": 98, "peopl": [98, 105], "pez": 98, "picnic": 98, "pram": 98, "prai": 98, "pyramid": 98, "rainbow": 98, "roulett": 98, "saddl": 98, "saturn": 98, "segwai": 98, "propel": 98, "sextant": 98, "music": 98, "skateboard": 98, "smokestack": 98, "sneaker": 98, "boat": 98, "stain": 98, "steer": 98, "stirrup": 98, "superman": 98, "sushi": 98, "armi": [98, 110], "sword": 98, "tambourin": 98, "teepe": 98, "court": 98, "theodolit": 98, "tomato": 98, "tombston": 98, "tour": 98, "pisa": 98, "treadmil": 98, "fork": 98, "tweezer": 98, "unicorn": 98, "vcr": 98, "waterfal": 98, "watermelon": 98, "weld": 98, "windmil": 98, "xylophon": 98, "yarmulk": 98, "yo": 98, "toad": 98, "twenty_news_test_set": 98, "comp": 98, "graphic": [98, 109], "misc": [98, 110], "sy": 98, "ibm": 98, "pc": 98, "hardwar": 98, "mac": 98, "forsal": 98, "rec": 98, "crypt": 98, "electron": 98, "med": 98, "soc": 98, "religion": 98, "christian": [98, 110], "talk": [98, 110], "polit": 98, "gun": 98, "mideast": 98, "amazon": 98, "neutral": 98, "imdb_test_set": 98, "all_class": 98, "20news_test_set": 98, "_load_classes_predprobs_label": 98, "dataset_nam": 98, "labelerror": 98, "url_bas": 98, "5392f6c71473055060be3044becdde1cbc18284d": 98, "url_label": 98, "original_test_label": 98, "_original_label": 98, "url_prob": 98, "cross_validated_predicted_prob": 98, "_pyx": 98, "num_part": 98, "datatset": 98, "bytesio": 98, "allow_pickl": 98, "pred_probs_part": 98, "url": 98, "_of_": 98, "nload": 98, "imdb": 98, "ve": [98, 99, 100, 101, 103, 105], "capit": 98, "29780": 98, "256": [98, 99, 100, 105], "780": 98, "medic": [98, 110], "doctor": 98, "254": [98, 105], "359223": 98, "640777": 98, "184": [98, 101], "258427": 98, "341176": 98, "263158": 98, "658824": 98, "337349": 98, "246575": 98, "662651": 98, "248": 98, "330000": 98, "355769": 98, "251": [98, 105], "167": [98, 101, 105], "252": [98, 100], "112": [98, 100], "253": [98, 105], "022989": 98, "049505": 98, "190": [98, 101, 105], "002216": 98, "000974": 98, "000873": 98, "000739": 98, "32635": 98, "32636": 98, "32637": 98, "32638": 98, "32639": 98, "32640": 98, "051": 98, "002242": 98, "997758": 98, "002088": 98, "001045": 98, "997912": 98, "002053": 98, "997947": 98, "001980": 98, "000991": 98, "998020": 98, "001946": 98, "002915": 98, "998054": 98, "001938": 98, "002904": 98, "998062": 98, "001020": 98, "998980": 98, "001018": 98, "002035": 98, "998982": 98, "999009": 98, "0003": 98, "0002": 98, "071": 98, "067269": 98, "929": 98, "046": 98, "058243": 98, "954": 98, "035": 98, "032096": 98, "031": 98, "012232": 98, "969": 98, "022": 98, "025896": 98, "978": 98, "020": [98, 101], "013092": 98, "018": 98, "013065": 98, "016": 98, "030542": 98, "984": 98, "013": 98, "020833": 98, "987": 98, "012": 98, "010020": 98, "988": 98, "0073": 98, "0020": 98, "0016": 98, "0015": 98, "0014": 98, "0013": 98, "0012": 98, "0010": 98, "0008": 98, "0007": 98, "0006": 98, "0005": 98, "0004": 98, "244": [98, 105], "452381": 98, "459770": 98, "523364": 98, "460784": 98, "446602": 98, "103774": 98, "030612": 98, "110092": 98, "049020": 98, "0034": 98, "0032": 98, "0026": 98, "0025": 98, "4945": 98, "4946": 98, "4947": 98, "4948": 98, "4949": 98, "4950": 98, "846": 98, "7532": 98, "532": 98, "034483": 98, "009646": 98, "965517": 98, "030457": 98, "020513": 98, "969543": 98, "028061": 98, "035443": 98, "971939": 98, "025316": 98, "005168": 98, "974684": 98, "049751": 98, "979487": 98, "019920": 98, "042802": 98, "980080": 98, "017677": 98, "005115": 98, "982323": 98, "012987": 98, "005236": 98, "987013": 98, "012723": 98, "025126": 98, "987277": 98, "010989": 98, "008264": 98, "989011": 98, "010283": 98, "027778": 98, "989717": 98, "009677": 98, "990323": 98, "007614": 98, "010127": 98, "992386": 98, "005051": 98, "994949": 98, "005025": 98, "994975": 98, "005013": 98, "994987": 98, "001859": 98, "001328": 98, "000929": 98, "000664": 98, "186": [98, 101], "188": [98, 101, 104], "189": [98, 101], "snippet": 99, "nlp": [99, 110], "mind": [99, 101], "alphanumer": 99, "facilit": 99, "seamless": 99, "classlabel": 99, "guidanc": 99, "labels_str": 99, "datalab_str": 99, "labels_int": 99, "remap": 99, "datalab_int": 99, "my_dict": 99, "pet_nam": 99, "rover": 99, "rocki": 99, "speci": 99, "datalab_dataset": 99, "number_of_class": 99, "total_number_of_data_point": 99, "feed": 99, "alphabet": 99, "labels_proper_format": 99, "your_classifi": 99, "issues_datafram": 99, "class_predicted_for_flagged_exampl": 99, "class_predicted_for_all_exampl": 99, "grant": 99, "On": [99, 100, 101, 105], "merged_dataset": 99, "label_column_nam": 99, "datataset": 99, "fair": [99, 101], "game": 99, "speedup": [99, 106], "tempfil": 99, "mkdtemp": 99, "sped": 99, "anywai": 99, "pred_probs_merg": 99, "merge_rare_class": 99, "count_threshold": 99, "class_mapping_orig2new": 99, "heath_summari": 99, "num_examples_per_class": 99, "rare_class": 99, "num_classes_merg": 99, "other_class": 99, "labels_merg": 99, "new_c": 99, "merged_prob": 99, "new_class": 99, "original_class": 99, "num_check": 99, "ones_array_ref": 99, "isclos": 99, "though": [99, 101, 110], "successfulli": 99, "virtuou": [99, 103], "cycl": [99, 103], "jointli": 99, "junk": 99, "clutter": 99, "unknown": 99, "caltech": 99, "combined_boolean_mask": 99, "mask1": 99, "mask2": 99, "gradientboostingclassifi": [99, 101], "true_error": [99, 101, 104], "101": [99, 100, 105], "102": [99, 104, 105], "104": [99, 101, 105], "model_to_find_error": 99, "model_to_return": 99, "cl0": 99, "randomizedsearchcv": 99, "expens": 99, "param_distribut": 99, "learning_r": [99, 100, 101], "max_depth": [99, 100, 101], "magnitud": 99, "coeffici": [99, 108], "optin": 99, "environ": [99, 100, 101], "rerun": [99, 100, 101], "cell": [99, 100, 101], "unabl": [99, 100, 101], "render": [99, 100, 101], "nbviewer": [99, 100, 101], "cleanlearninginot": [99, 101], "fittedcleanlearn": [99, 101], "linearregressionlinearregress": 99, "unexpectedli": 99, "emphas": 99, "crucial": 99, "merge_duplicate_set": 99, "merge_kei": 99, "construct_group_kei": 99, "merged_set": 99, "consolidate_set": 99, "issubset": 99, "frozenset": [99, 100], "sets_list": 99, "mutabl": 99, "new_set": 99, "current_set": 99, "intersecting_set": 99, "lowest_score_strategi": 99, "sub_df": 99, "filter_near_dupl": 99, "strategy_fn": 99, "strategy_kwarg": 99, "duplicate_row": 99, "group_kei": 99, "to_keep_indic": 99, "groupbi": 99, "explod": 99, "to_remov": 99, "isin": [99, 106], "kept": 99, "ids_to_remove_seri": 99, "assist": 99, "streamlin": [99, 100], "ux": 99, "agpl": 99, "compani": 99, "commerci": 99, "alter": [99, 100], "email": 99, "team": 99, "anywher": 99, "profession": 99, "expert": 99, "recogn": 100, "vital": 100, "leakag": 100, "comparion": 100, "leak": 100, "blueprint": 100, "divers": 100, "parameter": 100, "tldr": 100, "answer": [100, 101], "subtl": 100, "faith": 100, "danger": 100, "inevit": [100, 106], "xgbclassifi": 100, "123456": 100, "df_train": 100, "s3": [100, 105, 109, 110], "amazonaw": [100, 105, 109, 110], "clos_train_data": 100, "df_test": 100, "clos_test_data": 100, "noisy_letter_grad": 100, "018bff": 100, "076d92": 100, "c80059": 100, "e38f8a": 100, "d57e1a": 100, "grade_l": 100, "notes_l": 100, "train_featur": 100, "train_features_v2": 100, "train_labels_v2": 100, "test_featur": 100, "preprocessed_train_data": 100, "preprocessed_test_data": 100, "haven": 100, "features_df": 100, "heterogenou": 100, "full_df": 100, "reset_index": [100, 103], "749": 100, "583745": 100, "291382": 100, "5837": 100, "748": 100, "604": 100, "510": 100, "227": [100, 104, 105], "719": 100, "690": 100, "444": 100, "547": 100, "647": 100, "2914": 100, "611": 100, "687869": 100, "610": 100, "687883": 100, "612": 100, "688146": 100, "609": 100, "688189": 100, "613": 100, "688713": 100, "2913818469137725": 100, "came": [100, 110], "full_duplicate_result": 100, "train_idx_cutoff": 100, "nd_set_has_index_over_training_cutoff": 100, "exact_dupl": 100, "627": 100, "678": 100, "615": 100, "292": 100, "620": 100, "420": 100, "704": 100, "431": 100, "459": 100, "672": 100, "564": 100, "696": 100, "605": 100, "exact_duplicates_indic": 100, "indices_of_duplicates_to_drop": 100, "4a3f75": 100, "d030b5": 100, "ddd0ba": 100, "8e6d24": 100, "464aab": 100, "ee3387": 100, "61e807": 100, "71d7b9": 100, "83e31f": 100, "edeb53": 100, "cd52b5": 100, "84": [100, 105, 108], "454e51": 100, "042686": 100, "12a73f": 100, "tree_method": 100, "hist": [100, 106], "enable_categor": 100, "booster": 100, "callback": 100, "colsample_bylevel": 100, "colsample_bynod": 100, "colsample_bytre": 100, "early_stopping_round": 100, "eval_metr": 100, "feature_typ": 100, "gamma": 100, "grow_polici": 100, "importance_typ": 100, "interaction_constraint": 100, "max_bin": 100, "max_cat_threshold": 100, "max_cat_to_onehot": 100, "max_delta_step": 100, "max_leav": 100, "min_child_weight": 100, "monotone_constraint": 100, "multi_strategi": 100, "n_estim": [100, 101], "num_parallel_tre": 100, "x27": [100, 101], "softprob": 100, "xgbclassifierifittedxgbclassifi": 100, "test_pred_prob": [100, 106], "test_lab": 100, "test_features_arrai": 100, "134": 100, "798507": 100, "370259": 100, "625352": 100, "524042": 100, "097015": 100, "7985": 100, "000537": 100, "000903": 100, "001743": 100, "106": 100, "001853": 100, "002121": 100, "3703": 100, "752463e": 100, "784418e": 100, "477741e": 100, "134230e": 100, "153555e": 100, "6254": 100, "143272": 100, "146501": 100, "161431": 100, "5240": 100, "765240": 100, "771221": 100, "801589": 100, "801652": 100, "810735": 100, "5240417899434826": 100, "0970": 100, "na": [100, 103], "test_label_issue_result": 100, "test_label_issues_ord": 100, "2bd759": 100, "34ccdd": 100, "bb3bab": 100, "103": [100, 101, 105], "bf1b14": 100, "4787de": 100, "865cbd": 100, "32d53f": 100, "5b2f76": 100, "28f8b4": 100, "df814d": 100, "f17261": 100, "1db3ff": 100, "ded944": 100, "124": [100, 105], "343dd3": 100, "homework": [100, 108], "8d904d": 100, "e4f0d5": 100, "d6d208": 100, "76c083": 100, "695f96": 100, "745c23": 100, "13b36e": 100, "5ba892": 100, "9f0216": 100, "003628": 100, "004006": 100, "004031": 100, "007930": 100, "013226": 100, "015255": 100, "017692": 100, "019767": 100, "036197": 100, "054746": 100, "055110": 100, "062675": 100, "112695": 100, "121059": 100, "171280": 100, "181689": 100, "208001": 100, "275028": 100, "346032": 100, "396350": 100, "401493": 100, "474349": 100, "mislead": 100, "breviti": 100, "indices_to_drop_from_test_data": 100, "df_test_clean": 100, "acc_origin": 100, "tediou": 100, "train_features_arrai": 100, "train_lab": 100, "318": [100, 108], "601": 100, "740433": 100, "344154": 100, "588290": 100, "437267": 100, "146423": 100, "977223": 100, "7404": 100, "162": 100, "000072": 100, "348": 100, "000161": 100, "232": [100, 105], "000256": 100, "205": [100, 105], "000458": 100, "000738": 100, "3442": 100, "588": 100, "358961e": 100, "336": [100, 105], "490911e": 100, "269": 100, "122475e": 100, "321": [100, 105], "374139e": 100, "311": 100, "358617e": 100, "5883": 100, "600": 100, "592": 100, "593": 100, "594": 100, "595": 100, "596": 100, "597": 100, "598": 100, "599": 100, "221": 100, "222": [100, 101], "315": 100, "332": [100, 105], "791060e": 100, "243": [100, 105, 110], "540": 100, "379106e": 100, "396": 100, "397": 100, "398": 100, "399": 100, "4373": 100, "165": [100, 104], "550374": 100, "627357": 100, "627496": 100, "627502": 100, "627919": 100, "43726734378061227": 100, "1464": 100, "506": 100, "393": 100, "508": 100, "9772": 100, "402": 100, "401": 100, "aggress": 100, "faithfulli": 100, "label_issue_result": 100, "566": 100, "568": 100, "571": 100, "572": 100, "574": 100, "576": 100, "578": 100, "585": 100, "587": 100, "590": 100, "near_duplicates_idx": 100, "117": [100, 101, 108], "122": [100, 101, 105], "146": 100, "155": [100, 101, 105], "156": [100, 101], "173": [100, 105], "224": [100, 105], "272": 100, "277": [100, 105], "279": [100, 105], "288": 100, "300": [100, 103, 110], "342": 100, "352": 100, "365": 100, "366": 100, "384": 100, "388": 100, "394": 100, "404": 100, "474": 100, "480": 100, "494": 100, "515": 100, "536": 100, "537": 100, "539": 100, "542": 100, "outliers_idx": 100, "143": [100, 104, 105], "159": [100, 104, 105], "163": [100, 101], "193": [100, 101], "194": [100, 101], "208": 100, "240": [100, 105], "241": 100, "242": [100, 105], "247": [100, 105], "287": [100, 105], "295": [100, 105], "299": [100, 105], "307": [100, 105], "350": 100, "361": 100, "378": 100, "379": 100, "392": 100, "419": 100, "432": 100, "479": 100, "484": 100, "485": 100, "489": 100, "492": 100, "504": 100, "511": 100, "522": 100, "523": 100, "535": 100, "543": 100, "567": 100, "579": 100, "591": 100, "idx_to_drop": 100, "276": [100, 105], "df_train_cur": 100, "clean_clf": 100, "clean_pr": 100, "acc_clean": 100, "inaccur": 100, "hybrid": 100, "quantit": 100, "hyper": 100, "default_edit_param": 100, "drop_label_issu": 100, "drop_outli": 100, "drop_near_dupl": 100, "candid": [100, 105], "edit_data": 100, "percentag": [100, 101], "num_label_issues_to_drop": 100, "num_outliers_to_drop": 100, "dedupl": 100, "unique_clust": 100, "unique_clusters_list": 100, "near_duplicates_idx_to_drop": 100, "n_drop": 100, "label_issues_idx_to_drop": 100, "outliers_idx_to_drop": 100, "train_features_clean": 100, "train_labels_clean": 100, "itertool": 100, "finer": 100, "param_combin": 100, "best_scor": 100, "best_param": 100, "train_features_preprocess": 100, "train_labels_preprocess": 100, "depth": 101, "survei": [101, 110], "scienc": 101, "multivariate_norm": [101, 103, 104], "make_data": [101, 103], "cov": [101, 103, 104], "avg_trac": [101, 104], "py_tru": 101, "noise_matrix_tru": 101, "noise_marix": 101, "s_test": 101, "noisy_test_label": 101, "purpl": 101, "namespac": 101, "exec": 101, "markerfacecolor": [101, 104], "markeredgecolor": [101, 104, 108], "markers": [101, 104, 108], "markeredgewidth": [101, 104, 108], "realist": 101, "7560": 101, "637318e": 101, "896262e": 101, "548391e": 101, "923417e": 101, "375075e": 101, "3454": 101, "014051": 101, "020451": 101, "249": [101, 105], "042594": 101, "043859": 101, "045954": 101, "6120": 101, "023714": 101, "007136": 101, "119": [101, 105, 110], "107266": 101, "033738": 101, "238": [101, 105], "119505": 101, "236": [101, 105], "037843": 101, "614915": 101, "624422": 101, "625965": 101, "626079": 101, "118": 101, "627675": 101, "695223": 101, "323529": 101, "523015": 101, "013720": 101, "675727": 101, "646521": 101, "magic": 101, "liter": 101, "identif": 101, "logisticregressionlogisticregress": 101, "ever": 101, "092": 101, "040": 101, "024": 101, "004": 101, "surpris": 101, "1705": 101, "01936": 101, "ton": 101, "yourfavoritemodel1": 101, "merged_label": 101, "merged_test_label": 101, "newli": [101, 103], "yourfavoritemodel2": 101, "yourfavoritemodel3": 101, "cl3": 101, "takeawai": 101, "my_test_pred_prob": 101, "my_test_pr": 101, "issues_test": 101, "corrected_test_label": 101, "pretend": 101, "cl_test_pr": 101, "fairli": 101, "label_acc": 101, "offset": 101, "nquestion": 101, "overestim": 101, "experienc": 101, "prioiri": 101, "known": 101, "versatil": 101, "label_issues_indic": 101, "213": [101, 105], "218": [101, 105], "152": 101, "170": 101, "214": 101, "164": [101, 104], "191": [101, 105], "206": [101, 105], "115": [101, 105], "201": [101, 105], "174": 101, "150": [101, 103, 105], "169": 101, "151": [101, 105], "168": 101, "precision_scor": 101, "recall_scor": 101, "f1_score": 101, "true_label_issu": 101, "filter_by_list": 101, "718750": [101, 103], "807018": 101, "912": 101, "733333": 101, "800000": 101, "721311": 101, "792793": 101, "908": 101, "676923": 101, "765217": 101, "892": 101, "567901": 101, "702290": 101, "844": 101, "gaug": 101, "label_issues_count": 101, "172": [101, 104], "157": 101, "easiest": 101, "modular": 101, "penalti": 101, "l2": 101, "model3": 101, "cv_pred_probs_1": 101, "cv_pred_probs_2": 101, "cv_pred_probs_3": 101, "label_quality_scores_best": 101, "cv_pred_probs_ensembl": 101, "label_quality_scores_bett": 101, "superior": [101, 107], "timm": 102, "glad": 103, "multiannotator_label": 103, "noisier": 103, "local_data": [103, 104], "true_labels_train": [103, 104], "noise_matrix_bett": 103, "noise_matrix_wors": 103, "transpos": [103, 106], "zfill": 103, "row_na_check": 103, "notna": 103, "a0001": 103, "a0002": 103, "a0003": 103, "a0004": 103, "a0005": 103, "a0006": 103, "a0007": 103, "a0008": 103, "a0009": 103, "a0010": 103, "a0041": 103, "a0042": 103, "a0043": 103, "a0044": 103, "a0045": 103, "a0046": 103, "a0047": 103, "a0048": 103, "a0049": 103, "a0050": 103, "60856743": 103, "41693214": 103, "40908785": 103, "87147629": 103, "64941785": 103, "10774851": 103, "0524466": 103, "71853246": 103, "37169848": 103, "66031048": 103, "multiannotator_util": 103, "crude": 103, "straight": 103, "majority_vote_label": 103, "736118": 103, "757751": 103, "782232": 103, "715565": 103, "824256": 103, "quality_annotator_a0001": 103, "quality_annotator_a0002": 103, "quality_annotator_a0003": 103, "quality_annotator_a0004": 103, "quality_annotator_a0005": 103, "quality_annotator_a0006": 103, "quality_annotator_a0007": 103, "quality_annotator_a0008": 103, "quality_annotator_a0009": 103, "quality_annotator_a0010": 103, "quality_annotator_a0041": 103, "quality_annotator_a0042": 103, "quality_annotator_a0043": 103, "quality_annotator_a0044": 103, "quality_annotator_a0045": 103, "quality_annotator_a0046": 103, "quality_annotator_a0047": 103, "quality_annotator_a0048": 103, "quality_annotator_a0049": 103, "quality_annotator_a0050": 103, "070564": 103, "216078": 103, "119188": 103, "alongisd": 103, "244981": 103, "208333": 103, "295979": 103, "294118": 103, "324197": 103, "310345": 103, "355316": 103, "346154": 103, "439732": 103, "480000": 103, "a0031": 103, "523205": 103, "580645": 103, "a0034": 103, "535313": 103, "607143": 103, "a0021": 103, "606999": 103, "a0015": 103, "609526": 103, "678571": 103, "a0011": 103, "621103": 103, "692308": 103, "improved_consensus_label": 103, "majority_vote_accuraci": 103, "cleanlab_label_accuraci": 103, "8581081081081081": 103, "9797297297297297": 103, "besid": 103, "sorted_consensus_quality_scor": 103, "worst_qual": 103, "better_qu": 103, "worst_quality_accuraci": 103, "better_quality_accuraci": 103, "9893238434163701": 103, "improved_pred_prob": 103, "treat": [103, 104, 108, 110], "analzi": 103, "copyright": 104, "advertis": 104, "violenc": 104, "nsfw": 104, "celeba": 104, "make_multilabel_data": 104, "boxes_coordin": 104, "box_multilabel": 104, "make_multi": 104, "bx1": 104, "by1": 104, "bx2": 104, "by2": 104, "label_list": 104, "ur": 104, "upper": 104, "inidx": 104, "logical_and": 104, "inv_d": 104, "labels_idx": 104, "true_labels_test": 104, "dict_unique_label": 104, "get_color_arrai": 104, "dcolor": 104, "aa4400": 104, "55227f": 104, "55a100": 104, "00ff00": 104, "007f7f": 104, "386b55": 104, "0000ff": 104, "y_onehot": 104, "single_class_label": 104, "stratifi": [104, 107], "kf": 104, "train_index": 104, "test_index": 104, "clf_cv": 104, "x_train_cv": 104, "x_test_cv": 104, "y_train_cv": 104, "y_test_cv": 104, "y_pred_cv": 104, "saw": 104, "num_to_displai": 104, "275": 104, "267": 104, "225": 104, "171": [104, 110], "234": 104, "262": [104, 105], "263": [104, 105], "266": [104, 105], "139": 104, "216": [104, 105], "265": 104, "despit": [104, 110], "suspect": 104, "888": 104, "8224": 104, "9632": 104, "968": 104, "6512": 104, "0444": 104, "774": 104, "labels_binary_format": 104, "labels_list_format": 104, "surround": 105, "scene": 105, "coco": 105, "everydai": 105, "has_label_issu": 105, "objectdetectionbenchmark": 105, "tutorial_obj": 105, "pkl": 105, "example_imag": 105, "_separate_label": 105, "_separate_predict": 105, "begin": 105, "image_path": 105, "rb": 105, "image_to_visu": 105, "seg_map": 105, "334": 105, "bboxes_ignor": 105, "290": 105, "286": 105, "285": 105, "231": 105, "293": 105, "235": 105, "289": 105, "282": 105, "281": 105, "271": 105, "280": 105, "326": 105, "333": 105, "261": 105, "319": 105, "257": 105, "283": 105, "303": 105, "316": 105, "323": 105, "327": 105, "226": 105, "228": 105, "219": 105, "239": 105, "209": 105, "202": 105, "230": 105, "215": 105, "220": 105, "229": 105, "217": [105, 110], "237": 105, "207": [105, 110], "204": 105, "223": 105, "149": 105, "140": 105, "246": 105, "268": 105, "273": 105, "284": 105, "136": 105, "145": 105, "297": 105, "317": 105, "192": 105, "324": 105, "203": 105, "320": 105, "314": 105, "291": 105, "000000481413": 105, "jpg": 105, "42398": 105, "44503": 105, "29968": 105, "21005": 105, "9978472": 105, "forgot": 105, "drew": 105, "label_issue_idx": 105, "num_examples_to_show": 105, "138": 105, "97489622": 105, "70610878": 105, "98764951": 105, "88899237": 105, "99085805": 105, "issue_idx": 105, "95569726e": 105, "03354841e": 105, "57510169e": 105, "58447666e": 105, "39755858e": 105, "issue_to_visu": 105, "000000009483": 105, "95569726168054e": 105, "addition": [105, 109], "visibl": 105, "missmatch": 105, "likelei": 105, "agnost": 105, "vaidat": 105, "inconsist": 105, "000000395701": 105, "033548411774308e": 105, "armchair": 105, "tv": 105, "000000154004": 105, "38300759625496356": 105, "foreground": 105, "000000448410": 105, "0008575101690203273": 105, "crowd": 105, "alon": 105, "resembl": [105, 106], "000000499768": 105, "9748962231208227": 105, "000000521141": 105, "8889923658893665": 105, "000000143931": 105, "9876495074395956": 105, "bonu": 105, "uncov": 105, "irregular": 105, "object_detection_util": 105, "calculate_bounding_box_area": 105, "num_imgs_to_show": 105, "lab_object_count": 105, "pred_object_count": 105, "000000430073": 105, "000000183709": 105, "000000189475": 105, "label_norm": 105, "pred_norm": 105, "area": [105, 109], "lab_area": 105, "pred_area": 105, "lab_area_mean": 105, "lab_area_std": 105, "max_deviation_valu": 105, "max_deviation_class": 105, "deviation_valu": 105, "deviation_class": 105, "mean_area": 105, "std_area": 105, "class_area": 105, "deviations_awai": 105, "max_deviation_index": 105, "num_imgs_to_show_per_class": 105, "class_num": 105, "000000422886": 105, "000000341828": 105, "000000461009": 105, "train_feature_embed": 106, "ood_train_feature_scor": 106, "test_feature_embed": 106, "ood_test_feature_scor": 106, "ood_train_predictions_scor": 106, "train_pred_prob": 106, "ood_test_predictions_scor": 106, "pylab": 106, "rcparam": 106, "baggingclassifi": 106, "therebi": 106, "rescal": 106, "transform_norm": 106, "totensor": 106, "animal_class": 106, "non_animal_class": 106, "animal_idx": 106, "test_idx": 106, "toronto": 106, "edu": 106, "kriz": 106, "170498071": 106, "49690890": 106, "85it": 106, "plot_imag": 106, "visualize_outli": 106, "txt_class": 106, "npimg": 106, "show_label": 106, "data_subset": 106, "resnet50": 106, "corpu": 106, "2048": 106, "embed_imag": 106, "create_model": 106, "strang": 106, "odd": 106, "train_ood_features_scor": 106, "top_train_ood_features_idx": 106, "fun": 106, "negat": 106, "homogen": 106, "bottom_train_ood_features_idx": 106, "test_ood_features_scor": 106, "top_ood_features_idx": 106, "trade": 106, "5th": 106, "percentil": 106, "fifth_percentil": 106, "plt_rang": 106, "train_outlier_scor": 106, "test_outlier_scor": 106, "ood_features_indic": 106, "revisit": 106, "return_invers": 106, "train_feature_embeddings_sc": 106, "test_feature_embeddings_sc": 106, "train_pred_label": 106, "9702": 106, "train_ood_predictions_scor": 106, "test_ood_predictions_scor": 106, "lost": 106, "unsuit": 107, "convention": 107, "aforement": 107, "hypothet": 107, "contrast": 107, "tradit": 107, "disjoint": 107, "out_of_sample_pred_probs_for_a": 107, "out_of_sample_pred_probs_for_b": 107, "out_of_sample_pred_probs_for_c": 107, "out_of_sample_pred_prob": 107, "unsur": 107, "price": 108, "incom": 108, "sensor": 108, "histgradientboostingregressor": 108, "r2_score": 108, "student_grades_r": 108, "final_scor": 108, "true_final_scor": 108, "3d": 108, "mpl_toolkit": 108, "mplot3d": 108, "axes3d": 108, "errors_idx": 108, "add_subplot": 108, "z": 108, "errors_mask": 108, "feature_column": 108, "predicted_column": 108, "x_train_raw": 108, "x_test_raw": 108, "randomforestregressor": 108, "385101": 108, "499503": 108, "698255": 108, "776647": 108, "109373": 108, "170547": 108, "481096": 108, "984759": 108, "645270": 108, "795928": 108, "141": 108, "659": 108, "367": 108, "305": 108, "560": 108, "657": 108, "view_datapoint": 108, "preds_og": 108, "r2_og": 108, "838": 108, "found_label_issu": 108, "preds_cl": 108, "r2_cl": 108, "926": 108, "favorit": 108, "968627e": 108, "228799": 108, "646674e": 108, "402962": 108, "323818e": 108, "952758": 108, "422144e": 108, "456908": 108, "465815e": 108, "753968": 108, "791186e": 108, "110719": 108, "485156e": 108, "670640": 108, "225300e": 108, "749976": 108, "499679e": 108, "947007": 108, "067882e": 108, "648396": 108, "synthia": 109, "imagesegment": 109, "given_mask": 109, "predicted_mask": 109, "set_printopt": [109, 110], "sky": 109, "sidewalk": 109, "veget": 109, "terrain": 109, "rider": 109, "pred_probs_filepath": 109, "1088": 109, "1920": 109, "label_filepath": 109, "synthia_class": 109, "maunal": 109, "100000": 109, "244800": 109, "leftmost": 109, "middl": [109, 110], "infact": 109, "rightmost": 109, "discrep": 109, "3263230": 109, "783381": 109, "275110": 109, "255917": 109, "78225": 109, "55990": 109, "54315": 109, "33591": 109, "24645": 109, "21054": 109, "15045": 109, "14171": 109, "13832": 109, "13498": 109, "11490": 109, "9164": 109, "8769": 109, "6999": 109, "6031": 109, "5011": 109, "mistakenli": 109, "class_issu": 109, "aim": [109, 110], "domin": 109, "bunch": 110, "conll": 110, "2003": 110, "love": 110, "n_i": 110, "optional_list_of_ordered_class_nam": 110, "deepai": 110, "conll2003": 110, "rm": 110, "tokenclassif": 110, "2400": 110, "52e0": 110, "1a00": 110, "940": 110, "982975": 110, "960k": 110, "959": 110, "94k": 110, "inflat": 110, "17045998": 110, "16m": 110, "octet": 110, "26m": 110, "2mb": 110, "bert": 110, "read_npz": 110, "filepath": 110, "corrsespond": 110, "iob2": 110, "given_ent": 110, "entity_map": 110, "readfil": 110, "startswith": 110, "docstart": 110, "isalpha": 110, "isupp": 110, "indices_to_preview": 110, "nsentenc": 110, "eu": 110, "reject": 110, "boycott": 110, "british": 110, "lamb": 110, "00030412": 110, "00023826": 110, "99936208": 110, "00007009": 110, "00002545": 110, "99998795": 110, "00000401": 110, "00000218": 110, "00000455": 110, "00000131": 110, "00000749": 110, "99996115": 110, "00001371": 110, "0000087": 110, "00000895": 110, "99998936": 110, "00000382": 110, "00000178": 110, "00000366": 110, "00000137": 110, "99999101": 110, "00000266": 110, "00000174": 110, "0000035": 110, "00000109": 110, "99998768": 110, "00000482": 110, "00000202": 110, "00000438": 110, "0000011": 110, "00000465": 110, "99996392": 110, "00001105": 110, "0000116": 110, "00000878": 110, "99998671": 110, "00000364": 110, "00000213": 110, "00000472": 110, "00000281": 110, "99999073": 110, "00000211": 110, "00000159": 110, "00000442": 110, "00000115": 110, "peter": 110, "blackburn": 110, "00000358": 110, "00000529": 110, "99995623": 110, "0000129": 110, "0000024": 110, "00001812": 110, "99994141": 110, "00001645": 110, "00002162": 110, "brussel": 110, "1996": 110, "00001172": 110, "00000821": 110, "00004661": 110, "0000618": 110, "99987167": 110, "99999061": 110, "00000201": 110, "00000195": 110, "00000408": 110, "00000135": 110, "2254": 110, "2907": 110, "19392": 110, "9962": 110, "8904": 110, "19303": 110, "12918": 110, "9256": 110, "11855": 110, "18392": 110, "20426": 110, "19402": 110, "14744": 110, "19371": 110, "4645": 110, "10331": 110, "9430": 110, "6143": 110, "18367": 110, "12914": 110, "todai": 110, "weather": 110, "march": 110, "scalfaro": 110, "northern": 110, "himself": 110, "said": 110, "germani": 110, "nastja": 110, "rysich": 110, "north": 110, "spla": 110, "fought": 110, "khartoum": 110, "govern": 110, "south": 110, "1983": 110, "autonomi": 110, "animist": 110, "region": 110, "moslem": 110, "arabis": 110, "mayor": 110, "antonio": 110, "gonzalez": 110, "garcia": 110, "revolutionari": 110, "wednesdai": 110, "troop": 110, "raid": 110, "farm": 110, "stole": 110, "rape": 110, "women": 110, "spring": 110, "chg": 110, "hrw": 110, "12pct": 110, "princ": 110, "photo": 110, "moment": 110, "spokeswoman": 110, "rainier": 110, "told": 110, "reuter": 110, "danila": 110, "carib": 110, "w224": 110, "equip": 110, "radiomet": 110, "earn": 110, "19996": 110, "london": 110, "denom": 110, "sale": 110, "uk": 110, "jp": 110, "fr": 110, "maccabi": 110, "hapoel": 110, "haifa": 110, "tel": 110, "aviv": 110, "hospit": 110, "rever": 110, "roman": 110, "cathol": 110, "nun": 110, "admit": 110, "calcutta": 110, "week": 110, "ago": 110, "fever": 110, "vomit": 110, "allianc": 110, "embattl": 110, "kabul": 110, "salang": 110, "highwai": 110, "mondai": 110, "tuesdai": 110, "suprem": 110, "council": 110, "led": 110, "jumbish": 110, "milli": 110, "movement": 110, "warlord": 110, "abdul": 110, "rashid": 110, "dostum": 110, "dollar": 110, "exchang": 110, "3570": 110, "12049": 110, "born": 110, "1937": 110, "provinc": 110, "anhui": 110, "dai": 110, "shanghai": 110, "citi": 110, "prolif": 110, "author": 110, "teacher": 110, "chines": 110, "16764": 110, "1990": 110, "historian": 110, "alan": 110, "john": 110, "percival": 110, "taylor": 110, "di": 110, "20446": 110, "pace": 110, "bowler": 110, "ian": 110, "harvei": 110, "claim": 110, "victoria": 110, "15514": 110, "cotti": 110, "osc": 110, "foreign": 110, "minist": 110, "7525": 110, "sultan": 110, "specter": 110, "crown": 110, "abdullah": 110, "defenc": 110, "aviat": 110, "jeddah": 110, "saudi": 110, "agenc": 110, "2288": 110, "hi": 110, "customari": 110, "outfit": 110, "champion": 110, "damp": 110, "scalp": 110, "canada": 110, "reign": 110, "olymp": 110, "donovan": 110, "bailei": 110, "1992": 110, "linford": 110, "christi": 110, "britain": 110, "1984": 110, "1988": 110, "carl": 110, "lewi": 110, "ambigi": 110, "punctuat": 110, "chicago": 110, "digest": 110, "philadelphia": 110, "usda": 110, "york": 110, "token_issu": 110, "471": 110, "kean": 110, "year": 110, "contract": 110, "manchest": 110, "19072": 110, "societi": 110, "bite": 110, "deliv": 110, "19910": 110, "father": 110, "clarenc": 110, "woolmer": 110, "renam": 110, "uttar": 110, "pradesh": 110, "india": 110, "ranji": 110, "trophi": 110, "nation": 110, "championship": 110, "captain": 110, "1949": 110, "15658": 110, "19879": 110, "iii": 110, "brian": 110, "shimer": 110, "randi": 110, "jone": 110, "19104": 110}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [39, 0, 0, "-", "dataset"], [42, 0, 0, "-", "experimental"], [46, 0, 0, "-", "filter"], [47, 0, 0, "-", "internal"], [61, 0, 0, "-", "models"], [63, 0, 0, "-", "multiannotator"], [66, 0, 0, "-", "multilabel_classification"], [69, 0, 0, "-", "object_detection"], [72, 0, 0, "-", "outlier"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "regression"], [78, 0, 0, "-", "segmentation"], [82, 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"], [18, 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.adapter": [[13, 0, 0, "-", "imagelab"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, 2, 1, "", "CorrelationReporter"], [13, 2, 1, "", "CorrelationVisualizer"], [13, 2, 1, "", "ImagelabDataIssuesAdapter"], [13, 2, 1, "", "ImagelabIssueFinderAdapter"], [13, 2, 1, "", "ImagelabReporterAdapter"], [13, 1, 1, "", "create_imagelab"], [13, 1, 1, "", "handle_spurious_correlations"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter": [[13, 3, 1, "", "report"]], "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer": [[13, 3, 1, "", "visualize"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "filter_based_on_max_prevalence"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter": [[13, 3, 1, "", "get_report"], [13, 3, 1, "", "report"]], "cleanlab.datalab.internal": [[15, 0, 0, "-", "data"], [16, 0, 0, "-", "data_issues"], [19, 0, 0, "-", "issue_finder"], [17, 0, 0, "-", "issue_manager_factory"], [35, 0, 0, "-", "model_outputs"], [36, 0, 0, "-", "report"], [37, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[15, 2, 1, "", "Data"], [15, 5, 1, "", "DataFormatError"], [15, 5, 1, "", "DatasetDictError"], [15, 5, 1, "", "DatasetLoadError"], [15, 2, 1, "", "Label"], [15, 2, 1, "", "MultiClass"], [15, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[15, 3, 1, "", "add_note"], [15, 6, 1, "", "args"], [15, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[15, 4, 1, "", "class_names"], [15, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[16, 2, 1, "", "DataIssues"], [16, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[16, 3, 1, "", "collect_issues_from_imagelab"], [16, 3, 1, "", "collect_issues_from_issue_manager"], [16, 3, 1, "", "collect_statistics"], [16, 3, 1, "", "get_info"], [16, 3, 1, "", "get_issue_summary"], [16, 3, 1, "", "get_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_summary"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "set_health_score"], [16, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[19, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[21, 0, 0, "-", "data_valuation"], [22, 0, 0, "-", "duplicate"], [23, 0, 0, "-", "imbalance"], [25, 0, 0, "-", "issue_manager"], [26, 0, 0, "-", "label"], [29, 0, 0, "-", "noniid"], [30, 0, 0, "-", "null"], [31, 0, 0, "-", "outlier"], [34, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[21, 6, 1, "", "DEFAULT_THRESHOLD"], [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.duplicate": [[22, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 6, 1, "", "near_duplicate_sets"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[25, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[26, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_health_summary"], [26, 6, 1, "", "health_summary_parameters"], [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.multilabel": [[28, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[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.noniid": [[29, 2, 1, "", "NonIIDIssueManager"], [29, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[30, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[31, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "metric"], [31, 6, 1, "", "ood"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[33, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, 2, 1, "", "RegressionLabelIssueManager"], [33, 1, 1, "", "find_issues_with_features"], [33, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[33, 3, 1, "", "collect_info"], [33, 6, 1, "", "description"], [33, 3, 1, "", "find_issues"], [33, 6, 1, "", "info"], [33, 6, 1, "", "issue_name"], [33, 6, 1, "", "issue_score_key"], [33, 6, 1, "", "issues"], [33, 3, 1, "", "make_summary"], [33, 3, 1, "", "report"], [33, 6, 1, "", "summary"], [33, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[34, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [34, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [34, 3, 1, "", "collect_info"], [34, 6, 1, "", "description"], [34, 3, 1, "", "filter_cluster_ids"], [34, 3, 1, "", "find_issues"], [34, 3, 1, "", "get_underperforming_clusters"], [34, 6, 1, "", "info"], [34, 6, 1, "", "issue_name"], [34, 6, 1, "", "issue_score_key"], [34, 6, 1, "", "issues"], [34, 3, 1, "", "make_summary"], [34, 3, 1, "", "perform_clustering"], [34, 3, 1, "", "report"], [34, 6, 1, "", "summary"], [34, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, 7, 1, "", "REGISTRY"], [17, 1, 1, "", "list_default_issue_types"], [17, 1, 1, "", "list_possible_issue_types"], [17, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[35, 2, 1, "", "ModelOutput"], [35, 2, 1, "", "MultiClassPredProbs"], [35, 2, 1, "", "MultiLabelPredProbs"], [35, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[35, 6, 1, "", "argument"], [35, 3, 1, "", "collect"], [35, 6, 1, "", "data"], [35, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[36, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[36, 3, 1, "", "get_report"], [36, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[37, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[37, 6, 1, "", "CLASSIFICATION"], [37, 6, 1, "", "MULTILABEL"], [37, 6, 1, "", "REGRESSION"], [37, 3, 1, "", "__contains__"], [37, 3, 1, "", "__getitem__"], [37, 3, 1, "", "__iter__"], [37, 3, 1, "", "__len__"], [37, 3, 1, "", "from_str"], [37, 4, 1, "", "is_classification"], [37, 4, 1, "", "is_multilabel"], [37, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[39, 1, 1, "", "find_overlapping_classes"], [39, 1, 1, "", "health_summary"], [39, 1, 1, "", "overall_label_health_score"], [39, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[40, 0, 0, "-", "cifar_cnn"], [41, 0, 0, "-", "coteaching"], [43, 0, 0, "-", "label_issues_batched"], [44, 0, 0, "-", "mnist_pytorch"], [45, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[40, 2, 1, "", "CNN"], [40, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[40, 6, 1, "", "T_destination"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "add_module"], [40, 3, 1, "", "apply"], [40, 3, 1, "", "bfloat16"], [40, 3, 1, "", "buffers"], [40, 6, 1, "", "call_super_init"], [40, 3, 1, "", "children"], [40, 3, 1, "", "compile"], [40, 3, 1, "", "cpu"], [40, 3, 1, "", "cuda"], [40, 3, 1, "", "double"], [40, 6, 1, "", "dump_patches"], [40, 3, 1, "", "eval"], [40, 3, 1, "", "extra_repr"], [40, 3, 1, "", "float"], [40, 3, 1, "id0", "forward"], [40, 3, 1, "", "get_buffer"], [40, 3, 1, "", "get_extra_state"], [40, 3, 1, "", "get_parameter"], [40, 3, 1, "", "get_submodule"], [40, 3, 1, "", "half"], [40, 3, 1, "", "ipu"], [40, 3, 1, "", "load_state_dict"], [40, 3, 1, "", "modules"], [40, 3, 1, "", "named_buffers"], [40, 3, 1, "", "named_children"], [40, 3, 1, "", "named_modules"], [40, 3, 1, "", "named_parameters"], [40, 3, 1, "", "parameters"], [40, 3, 1, "", "register_backward_hook"], [40, 3, 1, "", "register_buffer"], [40, 3, 1, "", "register_forward_hook"], [40, 3, 1, "", "register_forward_pre_hook"], [40, 3, 1, "", "register_full_backward_hook"], [40, 3, 1, "", "register_full_backward_pre_hook"], [40, 3, 1, "", "register_load_state_dict_post_hook"], [40, 3, 1, "", "register_module"], [40, 3, 1, "", "register_parameter"], [40, 3, 1, "", "register_state_dict_pre_hook"], [40, 3, 1, "", "requires_grad_"], [40, 3, 1, "", "set_extra_state"], [40, 3, 1, "", "share_memory"], [40, 3, 1, "", "state_dict"], [40, 3, 1, "", "to"], [40, 3, 1, "", "to_empty"], [40, 3, 1, "", "train"], [40, 6, 1, "", "training"], [40, 3, 1, "", "type"], [40, 3, 1, "", "xpu"], [40, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[41, 1, 1, "", "adjust_learning_rate"], [41, 1, 1, "", "evaluate"], [41, 1, 1, "", "forget_rate_scheduler"], [41, 1, 1, "", "initialize_lr_scheduler"], [41, 1, 1, "", "loss_coteaching"], [41, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[43, 2, 1, "", "LabelInspector"], [43, 7, 1, "", "adj_confident_thresholds_shared"], [43, 1, 1, "", "find_label_issues_batched"], [43, 7, 1, "", "labels_shared"], [43, 7, 1, "", "pred_probs_shared"], [43, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[43, 3, 1, "", "get_confident_thresholds"], [43, 3, 1, "", "get_label_issues"], [43, 3, 1, "", "get_num_issues"], [43, 3, 1, "", "get_quality_scores"], [43, 3, 1, "", "score_label_quality"], [43, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[44, 2, 1, "", "CNN"], [44, 2, 1, "", "SimpleNet"], [44, 1, 1, "", "get_mnist_dataset"], [44, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[44, 3, 1, "", "__init_subclass__"], [44, 6, 1, "", "batch_size"], [44, 6, 1, "", "dataset"], [44, 6, 1, "", "epochs"], [44, 3, 1, "id0", "fit"], [44, 3, 1, "", "get_metadata_routing"], [44, 3, 1, "", "get_params"], [44, 6, 1, "", "loader"], [44, 6, 1, "", "log_interval"], [44, 6, 1, "", "lr"], [44, 6, 1, "", "momentum"], [44, 6, 1, "", "no_cuda"], [44, 3, 1, "id1", "predict"], [44, 3, 1, "id4", "predict_proba"], [44, 6, 1, "", "seed"], [44, 3, 1, "", "set_fit_request"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "set_predict_proba_request"], [44, 3, 1, "", "set_predict_request"], [44, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[44, 6, 1, "", "T_destination"], [44, 3, 1, "", "__call__"], [44, 3, 1, "", "add_module"], [44, 3, 1, "", "apply"], [44, 3, 1, "", "bfloat16"], [44, 3, 1, "", "buffers"], [44, 6, 1, "", "call_super_init"], [44, 3, 1, "", "children"], [44, 3, 1, "", "compile"], [44, 3, 1, "", "cpu"], [44, 3, 1, "", "cuda"], [44, 3, 1, "", "double"], [44, 6, 1, "", "dump_patches"], [44, 3, 1, "", "eval"], [44, 3, 1, "", "extra_repr"], [44, 3, 1, "", "float"], [44, 3, 1, "", "forward"], [44, 3, 1, "", "get_buffer"], [44, 3, 1, "", "get_extra_state"], [44, 3, 1, "", "get_parameter"], [44, 3, 1, "", "get_submodule"], [44, 3, 1, "", "half"], [44, 3, 1, "", "ipu"], [44, 3, 1, "", "load_state_dict"], [44, 3, 1, "", "modules"], [44, 3, 1, "", "named_buffers"], [44, 3, 1, "", "named_children"], [44, 3, 1, "", "named_modules"], [44, 3, 1, "", "named_parameters"], [44, 3, 1, "", "parameters"], [44, 3, 1, "", "register_backward_hook"], [44, 3, 1, "", "register_buffer"], [44, 3, 1, "", "register_forward_hook"], [44, 3, 1, "", "register_forward_pre_hook"], [44, 3, 1, "", "register_full_backward_hook"], [44, 3, 1, "", "register_full_backward_pre_hook"], [44, 3, 1, "", "register_load_state_dict_post_hook"], [44, 3, 1, "", "register_module"], [44, 3, 1, "", "register_parameter"], [44, 3, 1, "", "register_state_dict_pre_hook"], [44, 3, 1, "", "requires_grad_"], [44, 3, 1, "", "set_extra_state"], [44, 3, 1, "", "share_memory"], [44, 3, 1, "", "state_dict"], [44, 3, 1, "", "to"], [44, 3, 1, "", "to_empty"], [44, 3, 1, "", "train"], [44, 6, 1, "", "training"], [44, 3, 1, "", "type"], [44, 3, 1, "", "xpu"], [44, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[45, 1, 1, "", "display_issues"], [45, 1, 1, "", "find_label_issues"], [45, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[46, 1, 1, "", "find_label_issues"], [46, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [46, 1, 1, "", "find_predicted_neq_given"], [46, 7, 1, "", "pred_probs_by_class"], [46, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[48, 0, 0, "-", "label_quality_utils"], [49, 0, 0, "-", "latent_algebra"], [50, 0, 0, "-", "multiannotator_utils"], [51, 0, 0, "-", "multilabel_scorer"], [52, 0, 0, "-", "multilabel_utils"], [53, 0, 0, "-", "neighbor"], [57, 0, 0, "-", "outlier"], [58, 0, 0, "-", "token_classification_utils"], [59, 0, 0, "-", "util"], [60, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[48, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, 1, 1, "", "compute_inv_noise_matrix"], [49, 1, 1, "", "compute_noise_matrix_from_inverse"], [49, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [49, 1, 1, "", "compute_py"], [49, 1, 1, "", "compute_py_inv_noise_matrix"], [49, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[50, 1, 1, "", "assert_valid_inputs_multiannotator"], [50, 1, 1, "", "assert_valid_pred_probs"], [50, 1, 1, "", "check_consensus_label_classes"], [50, 1, 1, "", "compute_soft_cross_entropy"], [50, 1, 1, "", "find_best_temp_scaler"], [50, 1, 1, "", "format_multiannotator_labels"], [50, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[51, 2, 1, "", "Aggregator"], [51, 2, 1, "", "ClassLabelScorer"], [51, 2, 1, "", "MultilabelScorer"], [51, 1, 1, "", "exponential_moving_average"], [51, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [51, 1, 1, "", "get_label_quality_scores"], [51, 1, 1, "", "multilabel_py"], [51, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[51, 3, 1, "", "__call__"], [51, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[51, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [51, 6, 1, "", "NORMALIZED_MARGIN"], [51, 6, 1, "", "SELF_CONFIDENCE"], [51, 3, 1, "", "__call__"], [51, 3, 1, "", "__contains__"], [51, 3, 1, "", "__getitem__"], [51, 3, 1, "", "__iter__"], [51, 3, 1, "", "__len__"], [51, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[51, 3, 1, "", "__call__"], [51, 3, 1, "", "aggregate"], [51, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[52, 1, 1, "", "get_onehot_num_classes"], [52, 1, 1, "", "int2onehot"], [52, 1, 1, "", "onehot2int"], [52, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[54, 0, 0, "-", "knn_graph"], [55, 0, 0, "-", "metric"], [56, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[54, 7, 1, "", "DEFAULT_K"], [54, 1, 1, "", "construct_knn_graph_from_index"], [54, 1, 1, "", "correct_knn_distances_and_indices"], [54, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [54, 1, 1, "", "correct_knn_graph"], [54, 1, 1, "", "create_knn_graph_and_index"], [54, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[55, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [55, 7, 1, "", "ROW_COUNT_CUTOFF"], [55, 1, 1, "", "decide_default_metric"], [55, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[57, 1, 1, "", "correct_precision_errors"], [57, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, 1, 1, "", "color_sentence"], [58, 1, 1, "", "filter_sentence"], [58, 1, 1, "", "get_sentence"], [58, 1, 1, "", "mapping"], [58, 1, 1, "", "merge_probs"], [58, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[59, 1, 1, "", "append_extra_datapoint"], [59, 1, 1, "", "clip_noise_rates"], [59, 1, 1, "", "clip_values"], [59, 1, 1, "", "compress_int_array"], [59, 1, 1, "", "confusion_matrix"], [59, 1, 1, "", "csr_vstack"], [59, 1, 1, "", "estimate_pu_f1"], [59, 1, 1, "", "extract_indices_tf"], [59, 1, 1, "", "force_two_dimensions"], [59, 1, 1, "", "format_labels"], [59, 1, 1, "", "get_missing_classes"], [59, 1, 1, "", "get_num_classes"], [59, 1, 1, "", "get_unique_classes"], [59, 1, 1, "", "is_tensorflow_dataset"], [59, 1, 1, "", "is_torch_dataset"], [59, 1, 1, "", "num_unique_classes"], [59, 1, 1, "", "print_inverse_noise_matrix"], [59, 1, 1, "", "print_joint_matrix"], [59, 1, 1, "", "print_noise_matrix"], [59, 1, 1, "", "print_square_matrix"], [59, 1, 1, "", "remove_noise_from_class"], [59, 1, 1, "", "round_preserving_row_totals"], [59, 1, 1, "", "round_preserving_sum"], [59, 1, 1, "", "smart_display_dataframe"], [59, 1, 1, "", "subset_X_y"], [59, 1, 1, "", "subset_data"], [59, 1, 1, "", "subset_labels"], [59, 1, 1, "", "train_val_split"], [59, 1, 1, "", "unshuffle_tensorflow_dataset"], [59, 1, 1, "", "value_counts"], [59, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[60, 1, 1, "", "assert_indexing_works"], [60, 1, 1, "", "assert_nonempty_input"], [60, 1, 1, "", "assert_valid_class_labels"], [60, 1, 1, "", "assert_valid_inputs"], [60, 1, 1, "", "labels_to_array"], [60, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[62, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[62, 2, 1, "", "KerasWrapperModel"], [62, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[62, 3, 1, "", "fit"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "predict_proba"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[63, 1, 1, "", "convert_long_to_wide_dataset"], [63, 1, 1, "", "get_active_learning_scores"], [63, 1, 1, "", "get_active_learning_scores_ensemble"], [63, 1, 1, "", "get_label_quality_multiannotator"], [63, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [63, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[64, 0, 0, "-", "dataset"], [65, 0, 0, "-", "filter"], [67, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[64, 1, 1, "", "common_multilabel_issues"], [64, 1, 1, "", "multilabel_health_summary"], [64, 1, 1, "", "overall_multilabel_health_score"], [64, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, 1, 1, "", "find_label_issues"], [65, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[70, 1, 1, "", "compute_badloc_box_scores"], [70, 1, 1, "", "compute_overlooked_box_scores"], [70, 1, 1, "", "compute_swap_box_scores"], [70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"], [70, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[71, 1, 1, "", "bounding_box_size_distribution"], [71, 1, 1, "", "calculate_per_class_metrics"], [71, 1, 1, "", "class_label_distribution"], [71, 1, 1, "", "get_average_per_class_confusion_matrix"], [71, 1, 1, "", "get_sorted_bbox_count_idxs"], [71, 1, 1, "", "object_counts_per_image"], [71, 1, 1, "", "plot_class_distribution"], [71, 1, 1, "", "plot_class_size_distributions"], [71, 1, 1, "", "visualize"]], "cleanlab.outlier": [[72, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[72, 3, 1, "", "fit"], [72, 3, 1, "", "fit_score"], [72, 3, 1, "", "score"]], "cleanlab.rank": [[73, 1, 1, "", "find_top_issues"], [73, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [73, 1, 1, "", "get_label_quality_ensemble_scores"], [73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "get_normalized_margin_for_each_label"], [73, 1, 1, "", "get_self_confidence_for_each_label"], [73, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[75, 0, 0, "-", "learn"], [76, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[75, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[75, 3, 1, "", "__init_subclass__"], [75, 3, 1, "", "find_label_issues"], [75, 3, 1, "", "fit"], [75, 3, 1, "", "get_aleatoric_uncertainty"], [75, 3, 1, "", "get_epistemic_uncertainty"], [75, 3, 1, "", "get_label_issues"], [75, 3, 1, "", "get_metadata_routing"], [75, 3, 1, "", "get_params"], [75, 3, 1, "", "predict"], [75, 3, 1, "", "save_space"], [75, 3, 1, "", "score"], [75, 3, 1, "", "set_fit_request"], [75, 3, 1, "", "set_params"], [75, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[76, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[77, 0, 0, "-", "filter"], [79, 0, 0, "-", "rank"], [80, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[77, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[79, 1, 1, "", "get_label_quality_scores"], [79, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[80, 1, 1, "", "common_label_issues"], [80, 1, 1, "", "display_issues"], [80, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[81, 0, 0, "-", "filter"], [83, 0, 0, "-", "rank"], [84, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[81, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[83, 1, 1, "", "get_label_quality_scores"], [83, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[84, 1, 1, "", "common_label_issues"], [84, 1, 1, "", "display_issues"], [84, 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, 88, 89, 93, 95, 96, 99, 101, 104, 110], "count": [3, 101], "data_valu": [4, 21], "datalab": [5, 7, 9, 10, 12, 90, 91, 92, 93, 94, 95, 96, 97, 99, 101, 104], "creat": [7, 91, 92, 101, 103], "your": [7, 85, 91, 92, 96, 97, 99, 101], "own": 7, "issu": [7, 9, 10, 24, 33, 85, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 104, 105, 109, 110], "manag": [7, 24], "prerequisit": 7, "implement": 7, "issuemanag": [7, 91], "basic": 7, "check": [7, 85, 97, 100], "intermedi": 7, "advanc": [7, 91], "us": [7, 88, 89, 90, 92, 93, 95, 96, 97, 99, 100, 101, 103, 104, 105, 106, 108, 109, 110], "gener": [8, 97], "cluster": [8, 97, 99], "id": 8, "guid": [9, 12], "type": [9, 10, 101], "custom": [9, 91], "cleanlab": [9, 10, 85, 88, 89, 90, 93, 95, 96, 99, 101, 103, 104, 105, 106, 108, 109, 110], "studio": [9, 10], "easi": [9, 10, 85, 93], "mode": [9, 10, 85, 93], "can": [10, 92, 98, 99, 101, 103], "detect": [10, 90, 92, 93, 95, 96, 97, 99, 101, 105, 106], "estim": [10, 101, 103, 104], "each": 10, "input": 10, "label": [10, 26, 28, 33, 85, 88, 89, 90, 92, 93, 95, 96, 98, 99, 101, 103, 104, 105, 108, 109, 110], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 31, 57, 72, 93, 95, 96, 104, 106], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 92, 93, 95, 96], "duplic": [10, 22, 92, 93, 95, 96, 99, 104], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 96, 97], "iid": [10, 96, 97], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 86, 97, 101, 109], "imbal": [10, 23, 97], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 93, 97, 106], "specif": [10, 24, 109], "spuriou": [10, 97], "correl": [10, 97], "between": 10, "properti": 10, "score": [10, 97, 101, 103, 104, 105, 109, 110], "underperform": [10, 97, 99], "group": [10, 97, 99], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 30, 97], "is_null_issu": 10, "null_scor": 10, "data": [10, 15, 85, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110], "valuat": [10, 97], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 97], "paramet": [10, 101], "get": [12, 91, 92, 103, 104, 105, 109, 110], "start": [12, 98], "api": 12, "refer": 12, "imagelab": 13, "adapt": 14, "data_issu": 16, "factori": 17, "intern": [18, 47], "issue_find": 19, "issue_manag": [24, 25], "regist": 24, "ml": [24, 99, 100, 101], "task": [24, 37], "multilabel": 27, "noniid": 29, "regress": [32, 74, 75, 76, 99, 108], "prioriti": 33, "order": 33, "find": [33, 88, 89, 90, 92, 93, 95, 96, 97, 99, 101, 103, 104, 105, 106, 108, 109, 110], "underperforming_group": 34, "model_output": 35, "report": [36, 93], "dataset": [39, 64, 85, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104, 105, 106, 108, 109, 110], "cifar_cnn": 40, "coteach": 41, "experiment": 42, "label_issues_batch": 43, "mnist_pytorch": 44, "span_classif": 45, "filter": [46, 65, 68, 77, 81, 101], "label_quality_util": 48, "latent_algebra": 49, "multiannotator_util": 50, "multilabel_scor": 51, "multilabel_util": 52, "neighbor": 53, "knn_graph": 54, "metric": 55, "search": [56, 91], "token_classification_util": 58, "util": 59, "valid": [60, 93, 107], "model": [61, 85, 88, 89, 90, 93, 95, 96, 99, 100, 101, 103, 104, 105, 106, 108], "kera": 62, "multiannot": [63, 103], "multilabel_classif": 66, "rank": [67, 70, 73, 76, 79, 83, 101], "object_detect": 69, "summari": [71, 80, 84], "learn": [75, 92, 99, 101], "segment": [78, 109], "token_classif": [82, 110], "open": [85, 99], "sourc": [85, 99], "document": 85, "quickstart": 85, "1": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "instal": [85, 88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "2": [85, 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "all": [85, 92, 101], "sort": [85, 97], "3": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 104, 105, 106, 108, 109, 110], "handl": [85, 99], "error": [85, 89, 93, 99, 101, 103, 104, 105, 108, 109, 110], "train": [85, 88, 89, 90, 97, 99, 100, 106, 108], "robust": [85, 88, 89, 101, 108], "noisi": [85, 88, 89, 100, 101, 108], "4": [85, 88, 89, 90, 91, 92, 93, 95, 96, 97, 100, 101, 103, 105, 106, 108], "curat": [85, 100], "fix": [85, 99], "level": [85, 98, 101, 110], "5": [85, 88, 90, 92, 93, 95, 97, 100, 101, 103, 108], "improv": [85, 100, 103], "via": [85, 100, 101, 103], "mani": [85, 101], "other": [85, 103, 105, 108], "techniqu": [85, 100], "contribut": 85, "how": [86, 99, 101, 103, 104, 110], "migrat": 86, "version": 86, "0": 86, "from": [86, 88, 89, 91, 92, 100, 101, 108], "pre": [86, 90, 97, 99, 106], "function": [86, 91], "name": 86, "chang": 86, "modul": [86, 101], "new": 86, "remov": 86, "common": [86, 110], "argument": [86, 91], "variabl": 86, "cleanlearn": [87, 99, 101], "tutori": [87, 94, 98, 100, 102], "structur": 88, "tabular": [88, 95], "requir": [88, 89, 91, 92, 93, 95, 96, 103, 104, 105, 106, 108, 109, 110], "depend": [88, 89, 90, 91, 92, 93, 95, 96, 98, 100, 101, 103, 104, 105, 106, 108, 109, 110], "load": [88, 89, 90, 91, 92, 95, 96, 97, 108], "process": [88, 95, 106, 108], "select": [88, 95], "comput": [88, 90, 93, 95, 96, 97, 99, 100, 103, 107], "out": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "sampl": [88, 90, 91, 92, 93, 95, 96, 100, 103, 107], "predict": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 104, 105, 107], "probabl": [88, 90, 91, 92, 93, 95, 96, 97, 100, 103, 107], "more": [88, 89, 92, 101, 108], "spend": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "too": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "much": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "time": [88, 89, 92, 95, 96, 98, 101, 104, 106, 107, 108], "qualiti": [88, 89, 92, 95, 96, 98, 101, 103, 104, 105, 106, 107, 108, 109, 110], "text": [89, 96, 97, 110], "format": [89, 96, 99, 104, 105], "defin": [89, 93, 96, 97, 108], "potenti": [89, 103, 108], "an": [90, 93, 99], "audio": 90, "import": [90, 91, 92, 93, 98, 101, 103], "them": [90, 98, 100, 101], "speechbrain": 90, "featur": [90, 93, 106], "fit": 90, "linear": 90, "workflow": [91, 97, 101], "audit": [91, 92], "classifi": [91, 92, 97], "instanti": 91, "object": [91, 105], "increment": 91, "specifi": [91, 99], "nondefault": 91, "save": 91, "ad": 91, "A": 92, "unifi": 92, "kind": [92, 105], "skip": [92, 98, 101, 103], "detail": [92, 98, 101, 103], "about": 92, "addit": 92, "inform": [92, 93], "fetch": [93, 98], "normal": 93, "fashion": 93, "mnist": 93, "prepar": [93, 97], "k": [93, 95, 107], "fold": [93, 107], "cross": [93, 107], "embed": [93, 106], "7": [93, 100, 101], "view": 93, "most": [93, 110], "like": 93, "exampl": [93, 99, 101, 106], "sever": 93, "set": [93, 101], "dark": 93, "top": [93, 109], "low": 93, "numer": 95, "categor": [95, 97], "column": 95, "construct": 95, "nearest": 95, "neighbour": 95, "graph": [95, 97], "drift": [96, 104], "miscellan": 97, "acceler": 97, "knn": 97, "obtain": 97, "identifi": [97, 99, 100, 105], "explan": 97, "vector": 97, "perform": [97, 100], "visual": [97, 101, 105, 106, 109], "synthet": 97, "result": 97, "predefin": 97, "slice": [97, 99], "i": [97, 99, 101, 107], "catch": 97, "valu": 97, "encod": 97, "initi": [97, 103], "6": [97, 100, 101], "run": [97, 99], "analysi": [97, 105], "interpret": 97, "understand": 98, "evalu": [98, 100], "health": [98, 101], "8": [98, 100, 101], "popular": 98, "faq": 99, "what": [99, 101, 107], "do": [99, 101], "infer": 99, "correct": [99, 100], "ha": 99, "flag": 99, "should": 99, "v": [99, 100], "test": [99, 100, 101, 106], "big": 99, "limit": 99, "memori": 99, "why": [99, 100], "isn": 99, "t": 99, "work": [99, 101, 103, 110], "me": 99, "differ": [99, 105], "clean": [99, 100, 101], "final": 99, "hyperparamet": [99, 100], "tune": 99, "onli": 99, "one": [99, 101, 104, 109], "doe": [99, 103, 110], "take": 99, "so": 99, "long": 99, "when": [99, 101], "licens": 99, "under": 99, "answer": 99, "question": 99, "split": 100, "did": 100, "you": [100, 101], "make": 100, "thi": [100, 101], "preprocess": 100, "fundament": 100, "problem": 100, "setup": 100, "origin": 100, "baselin": 100, "manual": 100, "address": 100, "algorithm": 100, "better": [100, 103], "strategi": 100, "optim": 100, "9": 100, "conclus": 100, "The": 101, "centric": 101, "ai": 101, "machin": 101, "find_label_issu": 101, "line": 101, "code": 101, "twenti": 101, "lowest": 101, "see": 101, "now": 101, "let": 101, "": 101, "happen": 101, "we": 101, "merg": 101, "seafoam": 101, "green": 101, "yellow": 101, "re": 101, "One": 101, "rule": 101, "overal": [101, 109], "accur": 101, "directli": 101, "fulli": 101, "character": 101, "nois": 101, "matrix": [101, 104], "joint": 101, "prior": 101, "true": 101, "distribut": 101, "flip": 101, "rate": 101, "ani": 101, "again": 101, "support": 101, "lot": 101, "method": 101, "filter_bi": 101, "automat": 101, "everi": 101, "uniqu": 101, "num_label_issu": 101, "threshold": 101, "found": 101, "Not": 101, "sure": 101, "ensembl": 101, "multipl": [101, 103], "predictor": 101, "consensu": 103, "annot": 103, "major": 103, "vote": 103, "statist": 103, "compar": 103, "inspect": 103, "retrain": 103, "further": 103, "multi": 104, "beyond": 104, "mislabel": [104, 109, 110], "given": 104, "hot": 104, "binari": 104, "without": 104, "applic": 104, "real": 104, "download": [105, 109, 110], "objectlab": 105, "exploratori": 105, "pytorch": 106, "timm": 106, "cifar10": 106, "some": 106, "pred_prob": [106, 109, 110], "wai": 108, "semant": 109, "which": 109, "ar": 109, "commonli": 109, "focus": 109, "token": 110, "word": 110, "sentenc": 110, "contain": 110, "particular": 110}, "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"], [21, "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"]], "Spurious Correlations between image-specific properties and labels": [[10, "spurious-correlations-between-image-specific-properties-and-labels"]], "property": [[10, "property"]], "score": [[10, "score"]], "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"]], "Spurious Correlations Issue Parameters": [[10, "spurious-correlations-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "adapter": [[14, "adapter"]], "data": [[15, "module-cleanlab.datalab.internal.data"]], "data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[18, "internal"], [47, "internal"]], "issue_finder": [[19, "issue-finder"]], "duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[24, "issue-manager"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[24, "registered-issue-managers"]], "ML task-specific issue managers": [[24, "ml-task-specific-issue-managers"]], "label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[27, "multilabel"]], "noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[30, "null"]], "outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [57, "module-cleanlab.internal.outlier"], [72, "module-cleanlab.outlier"]], "regression": [[32, "regression"], [74, "regression"]], "Priority Order for finding issues:": [[33, null]], "underperforming_group": [[34, "underperforming-group"]], "model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[36, "report"]], "task": [[37, "task"]], "dataset": [[39, "module-cleanlab.dataset"], [64, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "experimental": [[42, "experimental"]], "label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "filter": [[46, "module-cleanlab.filter"], [65, "module-cleanlab.multilabel_classification.filter"], [68, "filter"], [77, "filter"], [81, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[53, "neighbor"]], "knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "search": [[56, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "util": [[59, "module-cleanlab.internal.util"]], "validation": [[60, "module-cleanlab.internal.validation"]], "models": [[61, "models"]], "keras": [[62, "module-cleanlab.models.keras"]], "multiannotator": [[63, "module-cleanlab.multiannotator"]], "multilabel_classification": [[66, "multilabel-classification"]], "rank": [[67, "module-cleanlab.multilabel_classification.rank"], [70, "module-cleanlab.object_detection.rank"], [73, "module-cleanlab.rank"], [79, "module-cleanlab.segmentation.rank"], [83, "module-cleanlab.token_classification.rank"]], "object_detection": [[69, "object-detection"]], "summary": [[71, "summary"], [80, "module-cleanlab.segmentation.summary"], [84, "module-cleanlab.token_classification.summary"]], "regression.learn": [[75, "module-cleanlab.regression.learn"]], "regression.rank": [[76, "module-cleanlab.regression.rank"]], "segmentation": [[78, "segmentation"]], "token_classification": [[82, "token-classification"]], "cleanlab open-source documentation": [[85, "cleanlab-open-source-documentation"]], "Quickstart": [[85, "quickstart"]], "1. Install cleanlab": [[85, "install-cleanlab"]], "2. Check your data for all sorts of issues": [[85, "check-your-data-for-all-sorts-of-issues"]], "3. Handle label errors and train robust models with noisy labels": [[85, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[85, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[85, "improve-your-data-via-many-other-techniques"]], "Contributing": [[85, "contributing"]], "Easy Mode": [[85, "easy-mode"], [93, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[86, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[86, "function-and-class-name-changes"]], "Module name changes": [[86, "module-name-changes"]], "New modules": [[86, "new-modules"]], "Removed modules": [[86, "removed-modules"]], "Common argument and variable name changes": [[86, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[87, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[88, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[88, "1.-Install-required-dependencies"], [89, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [96, "1.-Install-required-dependencies"], [108, "1.-Install-required-dependencies"]], "2. Load and process the data": [[88, "2.-Load-and-process-the-data"], [95, "2.-Load-and-process-the-data"], [108, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[88, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [95, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[88, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[88, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Spending too much time on data quality?": [[88, "Spending-too-much-time-on-data-quality?"], [89, "Spending-too-much-time-on-data-quality?"], [92, "Spending-too-much-time-on-data-quality?"], [95, "Spending-too-much-time-on-data-quality?"], [96, "Spending-too-much-time-on-data-quality?"], [98, "Spending-too-much-time-on-data-quality?"], [101, "Spending-too-much-time-on-data-quality?"], [104, "Spending-too-much-time-on-data-quality?"], [106, "Spending-too-much-time-on-data-quality?"], [107, "spending-too-much-time-on-data-quality"], [108, "Spending-too-much-time-on-data-quality?"]], "Text Classification with Noisy Labels": [[89, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[89, "2.-Load-and-format-the-text-dataset"], [96, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[89, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[89, "4.-Train-a-more-robust-model-from-noisy-labels"], [108, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[90, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[90, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[90, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[90, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[90, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[90, "5.-Use-cleanlab-to-find-label-issues"], [95, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[91, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[91, "Install-and-import-required-dependencies"]], "Create and load the data": [[91, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[91, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[91, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[91, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[91, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[91, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[91, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[92, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[92, "1.-Install-and-import-required-dependencies"], [93, "1.-Install-and-import-required-dependencies"], [103, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[92, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[92, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[92, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[92, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[92, "Get-additional-information"]], "Near duplicate issues": [[92, "Near-duplicate-issues"], [93, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[93, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[93, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[93, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[93, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[93, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[93, "7.-Use-cleanlab-to-find-issues"]], "View report": [[93, "View-report"]], "Label issues": [[93, "Label-issues"], [95, "Label-issues"], [96, "Label-issues"]], "View most likely examples with label errors": [[93, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[93, "Outlier-issues"], [95, "Outlier-issues"], [96, "Outlier-issues"]], "View most severe outliers": [[93, "View-most-severe-outliers"]], "View sets of near duplicate images": [[93, "View-sets-of-near-duplicate-images"]], "Dark images": [[93, "Dark-images"]], "View top examples of dark images": [[93, "View-top-examples-of-dark-images"]], "Low information images": [[93, "Low-information-images"]], "Datalab Tutorials": [[94, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[95, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[95, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[95, "Near-duplicate-issues"], [96, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[96, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[96, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[96, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[96, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[97, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[97, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[97, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[97, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[97, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[97, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[97, "Explanation:"]], "Data Valuation": [[97, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[97, "1.-Load-and-Prepare-the-Dataset"], [97, "id2"], [97, "id5"]], "2. Vectorize the Text Data": [[97, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[97, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[97, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[97, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[97, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[97, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [97, "id3"]], "3. (Optional) Cluster the Data": [[97, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[97, "4.-Identify-Underperforming-Groups-with-Datalab"], [97, "id4"]], "5. (Optional) Visualize the Results": [[97, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[97, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[97, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[97, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[97, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[97, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[97, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[97, "1.-Load-the-Dataset"], [97, "id8"]], "2: Encode Categorical Values": [[97, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[97, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[97, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[97, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[97, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[97, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[97, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[97, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[97, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[97, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Run Datalab Analysis": [[97, "2.-Run-Datalab-Analysis"]], "3. Interpret the Results": [[97, "3.-Interpret-the-Results"]], "Understanding Dataset-level Labeling Issues": [[98, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[98, "Install-dependencies-and-import-them"], [101, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[98, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[98, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[99, "FAQ"]], "What data can cleanlab detect issues in?": [[99, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[99, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[99, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[99, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[99, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[99, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[99, "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?": [[99, "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?": [[99, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[99, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[99, "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?": [[99, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[99, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[99, "Can't-find-an-answer-to-your-question?"]], "Improving ML Performance via Data Curation with Train vs Test Splits": [[100, "Improving-ML-Performance-via-Data-Curation-with-Train-vs-Test-Splits"]], "Why did you make this tutorial?": [[100, "Why-did-you-make-this-tutorial?"]], "1. Install dependencies": [[100, "1.-Install-dependencies"]], "2. Preprocess the data": [[100, "2.-Preprocess-the-data"]], "3. Check for fundamental problems in the train/test setup": [[100, "3.-Check-for-fundamental-problems-in-the-train/test-setup"]], "4. Train model with original (noisy) training data": [[100, "4.-Train-model-with-original-(noisy)-training-data"]], "Compute out-of-sample predicted probabilities for the test data from this baseline model": [[100, "Compute-out-of-sample-predicted-probabilities-for-the-test-data-from-this-baseline-model"]], "5. Check for issues in test data and manually address them": [[100, "5.-Check-for-issues-in-test-data-and-manually-address-them"]], "Use clean test data to evaluate the performance of model trained on noisy training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-noisy-training-data"]], "6. Check for issues in training data and algorithmically correct them": [[100, "6.-Check-for-issues-in-training-data-and-algorithmically-correct-them"]], "7. Train model on cleaned training data": [[100, "7.-Train-model-on-cleaned-training-data"]], "Use clean test data to evaluate the performance of model trained on cleaned training data": [[100, "Use-clean-test-data-to-evaluate-the-performance-of-model-trained-on-cleaned-training-data"]], "8. Identifying better training data curation strategies via hyperparameter optimization techniques": [[100, "8.-Identifying-better-training-data-curation-strategies-via-hyperparameter-optimization-techniques"]], "9. Conclusion": [[100, "9.-Conclusion"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[101, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[101, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[101, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[101, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[101, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[101, "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.": [[101, "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": [[101, "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": [[101, "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!": [[101, "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": [[101, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[101, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[101, "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)": [[101, "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:": [[101, "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": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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.": [[101, "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?": [[101, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[101, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[102, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[103, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[103, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[103, "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": [[103, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[103, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[103, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[103, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[103, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[103, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[104, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[104, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[104, "2.-Format-data,-labels,-and-model-predictions"], [105, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[104, "3.-Use-cleanlab-to-find-label-issues"], [105, "3.-Use-cleanlab-to-find-label-issues"], [109, "3.-Use-cleanlab-to-find-label-issues"], [110, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[104, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[104, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[104, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[104, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[104, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[105, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[105, "1.-Install-required-dependencies-and-download-data"], [109, "1.-Install-required-dependencies-and-download-data"], [110, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[105, "Get-label-quality-scores"], [109, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[105, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[105, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[105, "Other-uses-of-visualize"]], "Exploratory data analysis": [[105, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[106, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[106, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[106, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[106, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[106, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[106, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[107, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[107, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[107, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[108, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[108, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[108, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[109, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[109, "2.-Get-data,-labels,-and-pred_probs"], [110, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[109, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[109, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[109, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[110, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[110, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[110, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[110, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[110, "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.adapter.imagelab"], [15, "module-cleanlab.datalab.internal.data"], [16, "module-cleanlab.datalab.internal.data_issues"], [17, "module-cleanlab.datalab.internal.issue_manager_factory"], [18, "module-cleanlab.datalab.internal"], [19, "module-cleanlab.datalab.internal.issue_finder"], [21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [22, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [23, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [26, "module-cleanlab.datalab.internal.issue_manager.label"], [28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [29, "module-cleanlab.datalab.internal.issue_manager.noniid"], [30, "module-cleanlab.datalab.internal.issue_manager.null"], [31, "module-cleanlab.datalab.internal.issue_manager.outlier"], [33, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [35, "module-cleanlab.datalab.internal.model_outputs"], [36, "module-cleanlab.datalab.internal.report"], [37, "module-cleanlab.datalab.internal.task"], [39, "module-cleanlab.dataset"], [40, "module-cleanlab.experimental.cifar_cnn"], [41, "module-cleanlab.experimental.coteaching"], [42, "module-cleanlab.experimental"], [43, "module-cleanlab.experimental.label_issues_batched"], [44, "module-cleanlab.experimental.mnist_pytorch"], [45, "module-cleanlab.experimental.span_classification"], [46, "module-cleanlab.filter"], [47, "module-cleanlab.internal"], [48, "module-cleanlab.internal.label_quality_utils"], [49, "module-cleanlab.internal.latent_algebra"], [50, "module-cleanlab.internal.multiannotator_utils"], [51, "module-cleanlab.internal.multilabel_scorer"], [52, "module-cleanlab.internal.multilabel_utils"], [53, "module-cleanlab.internal.neighbor"], [54, "module-cleanlab.internal.neighbor.knn_graph"], [55, "module-cleanlab.internal.neighbor.metric"], [56, "module-cleanlab.internal.neighbor.search"], [57, "module-cleanlab.internal.outlier"], [58, "module-cleanlab.internal.token_classification_utils"], [59, "module-cleanlab.internal.util"], [60, "module-cleanlab.internal.validation"], [61, "module-cleanlab.models"], [62, "module-cleanlab.models.keras"], [63, "module-cleanlab.multiannotator"], [64, "module-cleanlab.multilabel_classification.dataset"], [65, "module-cleanlab.multilabel_classification.filter"], [66, "module-cleanlab.multilabel_classification"], [67, "module-cleanlab.multilabel_classification.rank"], [68, "module-cleanlab.object_detection.filter"], [69, "module-cleanlab.object_detection"], [70, "module-cleanlab.object_detection.rank"], [71, "module-cleanlab.object_detection.summary"], [72, "module-cleanlab.outlier"], [73, "module-cleanlab.rank"], [74, "module-cleanlab.regression"], [75, "module-cleanlab.regression.learn"], [76, "module-cleanlab.regression.rank"], [77, "module-cleanlab.segmentation.filter"], [78, "module-cleanlab.segmentation"], [79, "module-cleanlab.segmentation.rank"], [80, "module-cleanlab.segmentation.summary"], [81, "module-cleanlab.token_classification.filter"], [82, "module-cleanlab.token_classification"], [83, "module-cleanlab.token_classification.rank"], [84, "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"]], "correlationreporter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter"]], "correlationvisualizer (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer"]], "imagelabdataissuesadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter"]], "imagelabissuefinderadapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter"]], "imagelabreporteradapter (class in cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter"]], "cleanlab.datalab.internal.adapter.imagelab": [[13, "module-cleanlab.datalab.internal.adapter.imagelab"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.collect_statistics"]], "create_imagelab() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.create_imagelab"]], "filter_based_on_max_prevalence() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.filter_based_on_max_prevalence"]], "find_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.adapter.imagelab.imagelabissuefinderadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabIssueFinderAdapter.get_available_issue_types"]], "get_info() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.get_issues"]], "get_report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.get_report"]], "handle_spurious_correlations() (in module cleanlab.datalab.internal.adapter.imagelab)": [[13, "cleanlab.datalab.internal.adapter.imagelab.handle_spurious_correlations"]], "report() (cleanlab.datalab.internal.adapter.imagelab.correlationreporter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationReporter.report"]], "report() (cleanlab.datalab.internal.adapter.imagelab.imagelabreporteradapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabReporterAdapter.report"]], "set_health_score() (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.set_health_score"]], "statistics (cleanlab.datalab.internal.adapter.imagelab.imagelabdataissuesadapter property)": [[13, "cleanlab.datalab.internal.adapter.imagelab.ImagelabDataIssuesAdapter.statistics"]], "visualize() (cleanlab.datalab.internal.adapter.imagelab.correlationvisualizer method)": [[13, "cleanlab.datalab.internal.adapter.imagelab.CorrelationVisualizer.visualize"]], "data (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[15, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[15, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[15, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[15, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[15, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[15, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[15, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[15, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[15, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[15, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[15, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[15, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[15, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[16, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[16, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[16, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[17, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[17, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[18, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[19, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[19, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[21, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[22, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[23, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[25, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[26, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[28, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[29, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[30, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[31, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[33, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[33, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[34, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_underperforming_clusters() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_underperforming_clusters"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[34, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[35, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[35, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[35, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[35, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[36, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[36, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[36, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[37, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[37, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[37, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[37, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[37, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[39, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[39, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[40, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[40, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.forward"], [40, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[40, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[40, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[41, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[41, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[42, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[43, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[43, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[43, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[44, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [44, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[44, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [44, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [44, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[44, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[44, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[45, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[45, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[46, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[46, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[46, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[46, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[47, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[48, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[48, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[49, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[49, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[50, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[50, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[51, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[51, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[51, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[51, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[51, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[52, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[52, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[53, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[54, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "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)": [[54, "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)": [[54, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[54, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[55, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[55, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[56, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[56, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[57, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[57, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[58, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[58, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[59, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[59, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[60, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[60, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[61, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[62, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[62, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[62, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[62, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[63, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[63, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[64, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[64, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[65, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[65, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[66, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[67, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[67, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[68, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[68, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[69, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[70, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[70, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[71, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[71, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[72, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[72, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[72, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[73, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[73, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[73, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[74, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[75, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[75, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[75, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[75, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[76, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[76, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[77, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[77, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[78, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[79, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[79, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[80, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[80, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[81, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[81, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[82, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[83, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[83, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[84, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[84, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[84, "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 9d1517aad..1310c3072 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": "2024-09-06T19:32:51.069638Z",
-     "iopub.status.busy": "2024-09-06T19:32:51.069457Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.310694Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.310136Z"
+     "iopub.execute_input": "2024-09-26T14:46:49.976999Z",
+     "iopub.status.busy": "2024-09-26T14:46:49.976816Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.290105Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.289537Z"
     },
     "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:32:52.313494Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.312922Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.331174Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.330732Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.292353Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.291898Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.324181Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.323699Z"
     }
    },
    "outputs": [],
@@ -195,10 +195,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:52.333414Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.333012Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.616135Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.615552Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.326351Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.325915Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.516296Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.515685Z"
     }
    },
    "outputs": [
@@ -305,10 +305,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:52.647632Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.647448Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.650810Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.650339Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.550190Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.549718Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.556194Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.555704Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:52.652810Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.652474Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.660488Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.660065Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.558094Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.557801Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.566504Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.566057Z"
     }
    },
    "outputs": [],
@@ -384,10 +384,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:52.662789Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.662453Z",
-     "iopub.status.idle": "2024-09-06T19:32:52.664910Z",
-     "shell.execute_reply": "2024-09-06T19:32:52.664468Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.568613Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.568153Z",
+     "iopub.status.idle": "2024-09-26T14:46:51.571064Z",
+     "shell.execute_reply": "2024-09-26T14:46:51.570501Z"
     }
    },
    "outputs": [],
@@ -409,10 +409,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:52.667005Z",
-     "iopub.status.busy": "2024-09-06T19:32:52.666677Z",
-     "iopub.status.idle": "2024-09-06T19:32:53.186834Z",
-     "shell.execute_reply": "2024-09-06T19:32:53.186291Z"
+     "iopub.execute_input": "2024-09-26T14:46:51.573000Z",
+     "iopub.status.busy": "2024-09-26T14:46:51.572679Z",
+     "iopub.status.idle": "2024-09-26T14:46:52.105207Z",
+     "shell.execute_reply": "2024-09-26T14:46:52.104691Z"
     }
    },
    "outputs": [],
@@ -446,10 +446,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:53.189445Z",
-     "iopub.status.busy": "2024-09-06T19:32:53.189066Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.090605Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.089933Z"
+     "iopub.execute_input": "2024-09-26T14:46:52.107319Z",
+     "iopub.status.busy": "2024-09-26T14:46:52.107018Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.109749Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.109008Z"
     }
    },
    "outputs": [
@@ -481,10 +481,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.093443Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.092787Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.103390Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.102831Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.112432Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.111599Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.122786Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.122299Z"
     }
    },
    "outputs": [
@@ -605,10 +605,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.105571Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.105237Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.109432Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.108857Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.124636Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.124305Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.128711Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.128162Z"
     }
    },
    "outputs": [],
@@ -633,10 +633,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.111438Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.111142Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.120139Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.119708Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.130465Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.130125Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.138888Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.138403Z"
     }
    },
    "outputs": [],
@@ -658,10 +658,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.122107Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.121935Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.235206Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.234622Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.140677Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.140309Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.256974Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.256488Z"
     }
    },
    "outputs": [
@@ -691,10 +691,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.237464Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.237015Z",
-     "iopub.status.idle": "2024-09-06T19:32:55.240074Z",
-     "shell.execute_reply": "2024-09-06T19:32:55.239512Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.258978Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.258613Z",
+     "iopub.status.idle": "2024-09-26T14:46:54.261400Z",
+     "shell.execute_reply": "2024-09-26T14:46:54.260928Z"
     }
    },
    "outputs": [],
@@ -715,10 +715,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:55.242072Z",
-     "iopub.status.busy": "2024-09-06T19:32:55.241898Z",
-     "iopub.status.idle": "2024-09-06T19:32:57.303999Z",
-     "shell.execute_reply": "2024-09-06T19:32:57.303194Z"
+     "iopub.execute_input": "2024-09-26T14:46:54.263212Z",
+     "iopub.status.busy": "2024-09-26T14:46:54.262873Z",
+     "iopub.status.idle": "2024-09-26T14:46:56.458045Z",
+     "shell.execute_reply": "2024-09-26T14:46:56.457352Z"
     }
    },
    "outputs": [],
@@ -738,10 +738,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:57.307062Z",
-     "iopub.status.busy": "2024-09-06T19:32:57.306412Z",
-     "iopub.status.idle": "2024-09-06T19:32:57.318236Z",
-     "shell.execute_reply": "2024-09-06T19:32:57.317761Z"
+     "iopub.execute_input": "2024-09-26T14:46:56.460937Z",
+     "iopub.status.busy": "2024-09-26T14:46:56.459991Z",
+     "iopub.status.idle": "2024-09-26T14:46:56.471759Z",
+     "shell.execute_reply": "2024-09-26T14:46:56.471273Z"
     }
    },
    "outputs": [
@@ -786,10 +786,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-09-06T19:32:57.320219Z",
-     "iopub.status.busy": "2024-09-06T19:32:57.320039Z",
-     "iopub.status.idle": "2024-09-06T19:32:57.425487Z",
-     "shell.execute_reply": "2024-09-06T19:32:57.424961Z"
+     "iopub.execute_input": "2024-09-26T14:46:56.473438Z",
+     "iopub.status.busy": "2024-09-26T14:46:56.473240Z",
+     "iopub.status.idle": "2024-09-26T14:46:56.529027Z",
+     "shell.execute_reply": "2024-09-26T14:46:56.528533Z"
     },
     "nbsphinx": "hidden"
    },
@@ -827,7 +827,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 9f22a5e70..e78e1c343 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -821,7 +821,7 @@ 

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

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

@@ -884,43 +884,43 @@

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1223,7 +1223,7 @@

Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

The modern AI pipeline automated with Cleanlab Studio

diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index 7c3947e74..81bd9574d 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": "2024-09-06T19:33:00.675758Z", - "iopub.status.busy": "2024-09-06T19:33:00.675584Z", - "iopub.status.idle": "2024-09-06T19:33:03.510616Z", - "shell.execute_reply": "2024-09-06T19:33:03.510057Z" + "iopub.execute_input": "2024-09-26T14:47:00.005766Z", + "iopub.status.busy": "2024-09-26T14:47:00.005598Z", + "iopub.status.idle": "2024-09-26T14:47:03.458146Z", + "shell.execute_reply": "2024-09-26T14:47:03.457580Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:03.513184Z", - "iopub.status.busy": "2024-09-06T19:33:03.512761Z", - "iopub.status.idle": "2024-09-06T19:33:03.516199Z", - "shell.execute_reply": "2024-09-06T19:33:03.515742Z" + "iopub.execute_input": "2024-09-26T14:47:03.460102Z", + "iopub.status.busy": "2024-09-26T14:47:03.459810Z", + "iopub.status.idle": "2024-09-26T14:47:03.463418Z", + "shell.execute_reply": "2024-09-26T14:47:03.462845Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.518261Z", - "iopub.status.busy": "2024-09-06T19:33:03.517871Z", - "iopub.status.idle": "2024-09-06T19:33:03.520905Z", - "shell.execute_reply": "2024-09-06T19:33:03.520432Z" + "iopub.execute_input": "2024-09-26T14:47:03.465142Z", + "iopub.status.busy": "2024-09-26T14:47:03.464800Z", + "iopub.status.idle": "2024-09-26T14:47:03.467949Z", + "shell.execute_reply": "2024-09-26T14:47:03.467483Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.522785Z", - "iopub.status.busy": "2024-09-06T19:33:03.522608Z", - "iopub.status.idle": "2024-09-06T19:33:03.678565Z", - "shell.execute_reply": "2024-09-06T19:33:03.678029Z" + "iopub.execute_input": "2024-09-26T14:47:03.469646Z", + "iopub.status.busy": "2024-09-26T14:47:03.469283Z", + "iopub.status.idle": "2024-09-26T14:47:03.521848Z", + "shell.execute_reply": "2024-09-26T14:47:03.521259Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.680851Z", - "iopub.status.busy": "2024-09-06T19:33:03.680423Z", - "iopub.status.idle": "2024-09-06T19:33:03.684124Z", - "shell.execute_reply": "2024-09-06T19:33:03.683591Z" + "iopub.execute_input": "2024-09-26T14:47:03.523805Z", + "iopub.status.busy": "2024-09-26T14:47:03.523447Z", + "iopub.status.idle": "2024-09-26T14:47:03.527108Z", + "shell.execute_reply": "2024-09-26T14:47:03.526668Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.686150Z", - "iopub.status.busy": "2024-09-06T19:33:03.685759Z", - "iopub.status.idle": "2024-09-06T19:33:03.689186Z", - "shell.execute_reply": "2024-09-06T19:33:03.688640Z" + "iopub.execute_input": "2024-09-26T14:47:03.528762Z", + "iopub.status.busy": "2024-09-26T14:47:03.528492Z", + "iopub.status.idle": "2024-09-26T14:47:03.532073Z", + "shell.execute_reply": "2024-09-26T14:47:03.531625Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'change_pin', 'visa_or_mastercard', 'getting_spare_card', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'card_payment_fee_charged'}\n" + "Classes: {'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer', 'card_about_to_expire'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.691223Z", - "iopub.status.busy": "2024-09-06T19:33:03.690802Z", - "iopub.status.idle": "2024-09-06T19:33:03.693946Z", - "shell.execute_reply": "2024-09-06T19:33:03.693394Z" + "iopub.execute_input": "2024-09-26T14:47:03.533776Z", + "iopub.status.busy": "2024-09-26T14:47:03.533438Z", + "iopub.status.idle": "2024-09-26T14:47:03.536702Z", + "shell.execute_reply": "2024-09-26T14:47:03.536252Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.695918Z", - "iopub.status.busy": "2024-09-06T19:33:03.695618Z", - "iopub.status.idle": "2024-09-06T19:33:03.698740Z", - "shell.execute_reply": "2024-09-06T19:33:03.698281Z" + "iopub.execute_input": "2024-09-26T14:47:03.538408Z", + "iopub.status.busy": "2024-09-26T14:47:03.538094Z", + "iopub.status.idle": "2024-09-26T14:47:03.541437Z", + "shell.execute_reply": "2024-09-26T14:47:03.540871Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:03.700642Z", - "iopub.status.busy": "2024-09-06T19:33:03.700468Z", - "iopub.status.idle": "2024-09-06T19:33:08.790650Z", - "shell.execute_reply": "2024-09-06T19:33:08.789991Z" + "iopub.execute_input": "2024-09-26T14:47:03.543307Z", + "iopub.status.busy": "2024-09-26T14:47:03.542863Z", + "iopub.status.idle": "2024-09-26T14:47:08.488107Z", + "shell.execute_reply": "2024-09-26T14:47:08.487533Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "501ba738bb5947ccaad0e2cd1f842b14", + "model_id": "7bf569b1ec4240fbb7f1457722fe46c9", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31304fdb61a94d1eb88890ad65421b88", + "model_id": "dc4eb1dc64da457a9d83b0bad4f4fd96", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a73d7a796fe45fca51bb3d3b1eb08df", + "model_id": "47dd26560f0f4f14ae1d6235bf187f43", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4b2323ffd9349f1ad2d4d50a0288dc5", + "model_id": "6692a301895241f7894a3bace80aec4a", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "620076f191a74b5c914c7a2b17db4f55", + "model_id": "21332e3c65394cf38141b89a7102833d", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e6b938e7ce354e6ebb9c5105fe3bde01", + "model_id": "37b540a8453d4401b5a798e49297b5a2", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "122704b7d1124989a50bdf83f04c3039", + "model_id": "c550a7da6dee4658a5e958b278220075", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.793264Z", - "iopub.status.busy": "2024-09-06T19:33:08.793080Z", - "iopub.status.idle": "2024-09-06T19:33:08.795949Z", - "shell.execute_reply": "2024-09-06T19:33:08.795369Z" + "iopub.execute_input": "2024-09-26T14:47:08.490505Z", + "iopub.status.busy": "2024-09-26T14:47:08.490089Z", + "iopub.status.idle": "2024-09-26T14:47:08.493151Z", + "shell.execute_reply": "2024-09-26T14:47:08.492644Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.797847Z", - "iopub.status.busy": "2024-09-06T19:33:08.797676Z", - "iopub.status.idle": "2024-09-06T19:33:08.800380Z", - "shell.execute_reply": "2024-09-06T19:33:08.799925Z" + "iopub.execute_input": "2024-09-26T14:47:08.494934Z", + "iopub.status.busy": "2024-09-26T14:47:08.494590Z", + "iopub.status.idle": "2024-09-26T14:47:08.497376Z", + "shell.execute_reply": "2024-09-26T14:47:08.496905Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:08.802410Z", - "iopub.status.busy": "2024-09-06T19:33:08.802073Z", - "iopub.status.idle": "2024-09-06T19:33:11.565675Z", - "shell.execute_reply": "2024-09-06T19:33:11.564900Z" + "iopub.execute_input": "2024-09-26T14:47:08.499043Z", + "iopub.status.busy": "2024-09-26T14:47:08.498709Z", + "iopub.status.idle": "2024-09-26T14:47:11.411424Z", + "shell.execute_reply": "2024-09-26T14:47:11.410600Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.569102Z", - "iopub.status.busy": "2024-09-06T19:33:11.568193Z", - "iopub.status.idle": "2024-09-06T19:33:11.576067Z", - "shell.execute_reply": "2024-09-06T19:33:11.575576Z" + "iopub.execute_input": "2024-09-26T14:47:11.414407Z", + "iopub.status.busy": "2024-09-26T14:47:11.413552Z", + "iopub.status.idle": "2024-09-26T14:47:11.421686Z", + "shell.execute_reply": "2024-09-26T14:47:11.421111Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.578560Z", - "iopub.status.busy": "2024-09-06T19:33:11.578144Z", - "iopub.status.idle": "2024-09-06T19:33:11.582288Z", - "shell.execute_reply": "2024-09-06T19:33:11.581717Z" + "iopub.execute_input": "2024-09-26T14:47:11.423755Z", + "iopub.status.busy": "2024-09-26T14:47:11.423300Z", + "iopub.status.idle": "2024-09-26T14:47:11.427791Z", + "shell.execute_reply": "2024-09-26T14:47:11.427280Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.584328Z", - "iopub.status.busy": "2024-09-06T19:33:11.583988Z", - "iopub.status.idle": "2024-09-06T19:33:11.587376Z", - "shell.execute_reply": "2024-09-06T19:33:11.586902Z" + "iopub.execute_input": "2024-09-26T14:47:11.429713Z", + "iopub.status.busy": "2024-09-26T14:47:11.429285Z", + "iopub.status.idle": "2024-09-26T14:47:11.432675Z", + "shell.execute_reply": "2024-09-26T14:47:11.432217Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.589547Z", - "iopub.status.busy": "2024-09-06T19:33:11.589216Z", - "iopub.status.idle": "2024-09-06T19:33:11.592104Z", - "shell.execute_reply": "2024-09-06T19:33:11.591660Z" + "iopub.execute_input": "2024-09-26T14:47:11.434490Z", + "iopub.status.busy": "2024-09-26T14:47:11.434156Z", + "iopub.status.idle": "2024-09-26T14:47:11.437228Z", + "shell.execute_reply": "2024-09-26T14:47:11.436765Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.594213Z", - "iopub.status.busy": "2024-09-06T19:33:11.593882Z", - "iopub.status.idle": "2024-09-06T19:33:11.600605Z", - "shell.execute_reply": "2024-09-06T19:33:11.600152Z" + "iopub.execute_input": "2024-09-26T14:47:11.438815Z", + "iopub.status.busy": "2024-09-26T14:47:11.438635Z", + "iopub.status.idle": "2024-09-26T14:47:11.446087Z", + "shell.execute_reply": "2024-09-26T14:47:11.445615Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.602671Z", - "iopub.status.busy": "2024-09-06T19:33:11.602343Z", - "iopub.status.idle": "2024-09-06T19:33:11.828596Z", - "shell.execute_reply": "2024-09-06T19:33:11.828033Z" + "iopub.execute_input": "2024-09-26T14:47:11.447943Z", + "iopub.status.busy": "2024-09-26T14:47:11.447610Z", + "iopub.status.idle": "2024-09-26T14:47:11.721319Z", + "shell.execute_reply": "2024-09-26T14:47:11.720698Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:11.831240Z", - "iopub.status.busy": "2024-09-06T19:33:11.830841Z", - "iopub.status.idle": "2024-09-06T19:33:12.009186Z", - "shell.execute_reply": "2024-09-06T19:33:12.008615Z" + "iopub.execute_input": "2024-09-26T14:47:11.723702Z", + "iopub.status.busy": "2024-09-26T14:47:11.723282Z", + "iopub.status.idle": "2024-09-26T14:47:11.904646Z", + "shell.execute_reply": "2024-09-26T14:47:11.904102Z" }, "scrolled": true }, @@ -1073,10 +1073,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:12.011827Z", - "iopub.status.busy": "2024-09-06T19:33:12.011435Z", - "iopub.status.idle": "2024-09-06T19:33:12.015256Z", - "shell.execute_reply": "2024-09-06T19:33:12.014755Z" + "iopub.execute_input": "2024-09-26T14:47:11.906952Z", + "iopub.status.busy": "2024-09-26T14:47:11.906556Z", + "iopub.status.idle": "2024-09-26T14:47:11.910954Z", + "shell.execute_reply": "2024-09-26T14:47:11.910437Z" }, "nbsphinx": "hidden" }, @@ -1115,12 +1115,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0459048c4f99420aa195e714b5d9a0fd": { + "03473efde97f4db7aeb4bf94a8dcb6ac": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "06bdd0de78584b4d8c963a129ebf2a90": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1173,56 +1191,76 @@ "width": null } }, - "0828f51c6fc84a0da65b7ec09b69d580": { + "076de96beb79463ab6936bf1bcf942f5": { "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_60ed3679f364477387473b90089b8273", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9c60afb1733442418ab367430bbb0d68", - "tabbable": null, - "tooltip": null, - "value": 391.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "08df668bdad845f9a6fca994bd1ecd5d": { - "model_module": "@jupyter-widgets/controls", + "0988aba1120e44e194591113d57b63d3": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_66ad826330ec48dcbbb58f5f351a1112", - "placeholder": "​", - "style": "IPY_MODEL_8adb666a2a6e4976a7051e3074b29507", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "094ab3c4cb4649dbb35cf60d64c57d3d": { + "09d4dbd7007b4cfda79c92844565bfd3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1275,7 +1313,7 @@ "width": null } }, - "10da9ba1022e498b8f5f3f98c7898223": { + "0a9bbe4ae4a7460aa182a6b390e2126a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1328,49 +1366,92 @@ "width": null } }, - "122704b7d1124989a50bdf83f04c3039": { + "0b20d7b224384162a625ab6b3d0f9b75": { "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_f6577867a1c041f490b073388151f1be", - "IPY_MODEL_3e00fec335784d3d8f67aef8d5205c3a", - "IPY_MODEL_9692da0a0ad949f39b93867a9112ab58" - ], - "layout": "IPY_MODEL_c876ccec48d84a04bb874ff6b48f8030", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_159894c2bd95445e89e4038c12c6bb9f", + "placeholder": "​", + "style": "IPY_MODEL_cf82eb1ddad343378d886f2d760e8e19", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 95.5MB/s]" } }, - "154d34bbb18c4e6bb7471d21431c8407": { + "0bfe9f713e2d4b6ba3ef62402a269ffb": { "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_ecf47058d5624d17a0cd5595cc9a9ea1", + "placeholder": "​", + "style": "IPY_MODEL_5f805e6909fa4676b560df990b1225b3", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "12a2c923802042bbaf7afcb489317fc2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1fc253a07301484fbf34df123112adf2", + "placeholder": "​", + "style": "IPY_MODEL_2745511db571407b909eadcf1041fbbb", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 33.8MB/s]" + } + }, + "13323479403a4c6ba63c87932eb6b7d1": { + "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": "" } }, - "18fbcd7e9e6f47f89dbd1bf8da0aac40": { + "159894c2bd95445e89e4038c12c6bb9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1423,7 +1504,7 @@ "width": null } }, - "1b4847f4ce0741c391f613e88b131aaa": { + "16a4915303e24ee999326b95f314a277": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1476,67 +1557,60 @@ "width": null } }, - "27cccac5127445d09232f53a08657063": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "290f1d04116240fbaa62e5ec4b1a24a2": { - "model_module": "@jupyter-widgets/controls", + "17d77342577f4828b221978f61b1cca1": { + "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": "" - } - }, - "2b4d16d0c52d49b4b207e9e5d8450870": { - "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_0459048c4f99420aa195e714b5d9a0fd", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_49e660a0942a4523b89e94f5b3f10d5e", - "tabbable": null, - "tooltip": null, - "value": 466062.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "2fa841989e734ca59ab3392a1c472375": { + "18587e575408498d91cc167ce94971e8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1589,72 +1663,60 @@ "width": null } }, - "31304fdb61a94d1eb88890ad65421b88": { - "model_module": "@jupyter-widgets/controls", + "1b45076c459847edb676c5da860e7c63": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_87bd24bf12c243a6b9cca97e02ea6f70", - "IPY_MODEL_ea5aad74db984381a9502d15f7877dc9", - "IPY_MODEL_b917898a95b64706aca98aba5a2b9969" - ], - "layout": "IPY_MODEL_748657e3cb9543698e92e614f2b8352c", - "tabbable": null, - "tooltip": null - } - }, - "3248bde1a32e421da1664cd2d4d3419e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b8283fed28004000b84e2f53babadac1", - "placeholder": "​", - "style": "IPY_MODEL_8d94e369761f41d087d990935dbe60c2", - "tabbable": null, - "tooltip": null, - "value": "tokenizer.json: 100%" - } - }, - "34ddf006f73a4753b211a5999ec0d671": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "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 } }, - "390080de929440ada6a97f2e0d2dc60f": { + "1c9880baaa514557b939f1bc383fd6dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1672,33 +1734,7 @@ "text_color": null } }, - "3e00fec335784d3d8f67aef8d5205c3a": { - "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_908e91876b774d93a02042ae9035283d", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_290f1d04116240fbaa62e5ec4b1a24a2", - "tabbable": null, - "tooltip": null, - "value": 231508.0 - } - }, - "4071e96fc1124ad3bff4e7fe0f035295": { + "1f782846199a4cd886bc9a2d8b267db5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1751,57 +1787,7 @@ "width": null } }, - "470a184131ad4f9789eb904333469e81": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "49e660a0942a4523b89e94f5b3f10d5e": { - "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": "" - } - }, - "4bb99f8cedeb4182a94727c634341364": { - "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": "" - } - }, - "4cb28f8d4c2846bba02492de8371ef86": { + "1fc253a07301484fbf34df123112adf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1854,30 +1840,7 @@ "width": null } }, - "4dd3d95d46d846b98d4c8e1fca170cc1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f7f7d73c85274703989c1a758316f306", - "placeholder": "​", - "style": "IPY_MODEL_96ad5243a28540c1bbd13701050cd8c8", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 44.9MB/s]" - } - }, - "501ba738bb5947ccaad0e2cd1f842b14": { + "21332e3c65394cf38141b89a7102833d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1892,63 +1855,50 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_5b8638296ff64c2abc68c70b1b8b7469", - "IPY_MODEL_0828f51c6fc84a0da65b7ec09b69d580", - "IPY_MODEL_dc79b458bbac454fbab119272509e252" + "IPY_MODEL_723beeec471a4b999f642afe6f412d4f", + "IPY_MODEL_6a1338550c7e47f2ace913c6db6703dc", + "IPY_MODEL_60390240aff543d7947dd2f2f3272bde" ], - "layout": "IPY_MODEL_094ab3c4cb4649dbb35cf60d64c57d3d", + "layout": "IPY_MODEL_1b45076c459847edb676c5da860e7c63", "tabbable": null, "tooltip": null } }, - "5a73d7a796fe45fca51bb3d3b1eb08df": { + "2745511db571407b909eadcf1041fbbb": { "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_08df668bdad845f9a6fca994bd1ecd5d", - "IPY_MODEL_88b14d6b576d4c358576ada8914fc9ae", - "IPY_MODEL_efcccd1f66e4459cb1a7709eadb26866" - ], - "layout": "IPY_MODEL_18fbcd7e9e6f47f89dbd1bf8da0aac40", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5b8638296ff64c2abc68c70b1b8b7469": { + "27f46a8464f6451d9729e0085ec07516": { "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_dd603ce908534d83a9e5812536cbaecf", - "placeholder": "​", - "style": "IPY_MODEL_87f402b0764744998c823bf8713ee0ae", - "tabbable": null, - "tooltip": null, - "value": ".gitattributes: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "5bed445f56a545b89b799f73d2462bd9": { + "2b2f96f6470846f1bd8aae7cb5dbeed7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1964,137 +1914,48 @@ "description_width": "" } }, - "5f624de673e9405cb01619e550cd02b5": { - "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 - } - }, - "60ed3679f364477387473b90089b8273": { - "model_module": "@jupyter-widgets/base", + "2b379296834846fa97be92e4b2d6df99": { + "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 } }, - "620076f191a74b5c914c7a2b17db4f55": { + "2b51658eec434f6c8284bd799690104b": { "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_3248bde1a32e421da1664cd2d4d3419e", - "IPY_MODEL_2b4d16d0c52d49b4b207e9e5d8450870", - "IPY_MODEL_4dd3d95d46d846b98d4c8e1fca170cc1" - ], - "layout": "IPY_MODEL_4071e96fc1124ad3bff4e7fe0f035295", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e06dfc4b5e33464b8b271badc88425c4", + "placeholder": "​", + "style": "IPY_MODEL_1c9880baaa514557b939f1bc383fd6dd", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "vocab.txt: 100%" } }, - "626cdb8c3d374c168811bd920a5a68f8": { + "2b64f899a53543fb8a6355d358ad40dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2147,46 +2008,57 @@ "width": null } }, - "62cc1ededdbc4ebaa9a9455fc402d06e": { + "3127352d799744d391cc01252a89de9a": { "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_c1eada512714499a9689453bf4dd17bb", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_27f46a8464f6451d9729e0085ec07516", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "65182ed5c6764915b44ed26f3452c6e8": { + "37b540a8453d4401b5a798e49297b5a2": { "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_93a4c1dfcfc44589926437f1ffdd3a85", - "placeholder": "​", - "style": "IPY_MODEL_c4c2ce7cae784b2093ba53e3609cc2c9", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f85452b3a7ee4bd8b96746e8f9cbd2a8", + "IPY_MODEL_6431ceb1e80c48e09225d44e2f40b86d", + "IPY_MODEL_a8240a8e7877465086dc27694c472f92" + ], + "layout": "IPY_MODEL_604c0d5a866c4704a89f806366a61376", "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" + "tooltip": null } }, - "66ad826330ec48dcbbb58f5f351a1112": { + "3d76c17d5f61437bbb3f8fdefd0c78fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2222,42 +2094,24 @@ "height": null, "justify_content": null, "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "673761d4701d4ee5aaabc0e22e4ec6cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6b23b979b2f7419a924a8685e13b11a7": { + "3e46cd86409a4a129a6d5d33c5d96e40": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2310,25 +2164,7 @@ "width": null } }, - "73e88da4acc94040992fd88d0a0d19ed": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "748657e3cb9543698e92e614f2b8352c": { + "40e898ed62284e21bf36ba199efb74ca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2381,7 +2217,33 @@ "width": null } }, - "766a43c7b32e464cb876b478a34ad457": { + "4493cbda5ade4530a4f0dad6cc558cfa": { + "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_3d76c17d5f61437bbb3f8fdefd0c78fa", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_13323479403a4c6ba63c87932eb6b7d1", + "tabbable": null, + "tooltip": null, + "value": 54245363.0 + } + }, + "45bfcc5c5c3e4a10b411f71fb3237d72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2397,33 +2259,54 @@ "description_width": "" } }, - "84060f74615349bd9e7f70b839d37c3e": { + "46c6e93fc99f4795bfc2b82c11b1a4d6": { "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_6b23b979b2f7419a924a8685e13b11a7", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_766a43c7b32e464cb876b478a34ad457", + "layout": "IPY_MODEL_1f782846199a4cd886bc9a2d8b267db5", + "placeholder": "​", + "style": "IPY_MODEL_f350410541a947db9efae73d4e6d000d", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 665/665 [00:00<00:00, 129kB/s]" + } + }, + "47dd26560f0f4f14ae1d6235bf187f43": { + "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_adcd6fe3722a404cbb4578c43dfb8622", + "IPY_MODEL_6ef5403b4f904ab5bad25124da8132c1", + "IPY_MODEL_46c6e93fc99f4795bfc2b82c11b1a4d6" + ], + "layout": "IPY_MODEL_fb13c0f13bd34a04a46ba3462d87a3cb", + "tabbable": null, + "tooltip": null } }, - "846cc1a5fbd9438da4609b439141f308": { + "4e78d34c35c64a8ba9e42340cface541": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2476,30 +2359,25 @@ "width": null } }, - "87bd24bf12c243a6b9cca97e02ea6f70": { + "5d9fbb50ea574e9f80a04d65984ec3fb": { "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_fa4036e56d43472283e556f828cf84fd", - "placeholder": "​", - "style": "IPY_MODEL_470a184131ad4f9789eb904333469e81", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "87f402b0764744998c823bf8713ee0ae": { + "5f805e6909fa4676b560df990b1225b3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2517,33 +2395,30 @@ "text_color": null } }, - "88b14d6b576d4c358576ada8914fc9ae": { + "60390240aff543d7947dd2f2f3272bde": { "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_c634f1241a9e40659957b6a8dd57b66b", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4bb99f8cedeb4182a94727c634341364", + "layout": "IPY_MODEL_7d0ebaac4f0b43a498272ea2a11b15a3", + "placeholder": "​", + "style": "IPY_MODEL_2b379296834846fa97be92e4b2d6df99", "tabbable": null, "tooltip": null, - "value": 665.0 + "value": " 466k/466k [00:00<00:00, 14.0MB/s]" } }, - "89c1af10a3bf46378b2d5ad1570f4844": { + "604c0d5a866c4704a89f806366a61376": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2596,7 +2471,7 @@ "width": null } }, - "8adb666a2a6e4976a7051e3074b29507": { + "617bb9dddcf14ecb9bae9c187156bb6c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2614,7 +2489,57 @@ "text_color": null } }, - "8d94e369761f41d087d990935dbe60c2": { + "6431ceb1e80c48e09225d44e2f40b86d": { + "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_0988aba1120e44e194591113d57b63d3", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8944d62a93fe49f991ff8bacfeac322c", + "tabbable": null, + "tooltip": null, + "value": 48.0 + } + }, + "6692a301895241f7894a3bace80aec4a": { + "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_0bfe9f713e2d4b6ba3ef62402a269ffb", + "IPY_MODEL_4493cbda5ade4530a4f0dad6cc558cfa", + "IPY_MODEL_0b20d7b224384162a625ab6b3d0f9b75" + ], + "layout": "IPY_MODEL_18587e575408498d91cc167ce94971e8", + "tabbable": null, + "tooltip": null + } + }, + "69666c8921454e55aebdd3cb6ea39a73": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2632,60 +2557,77 @@ "text_color": null } }, - "908e91876b774d93a02042ae9035283d": { - "model_module": "@jupyter-widgets/base", + "6a1338550c7e47f2ace913c6db6703dc": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_06bdd0de78584b4d8c963a129ebf2a90", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_076de96beb79463ab6936bf1bcf942f5", + "tabbable": null, + "tooltip": null, + "value": 466062.0 + } + }, + "6b9cbd8a34764f13b850393a56c2c764": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6ef5403b4f904ab5bad25124da8132c1": { + "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_77b2da3b157a4e658101eddffc7a0c02", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2b2f96f6470846f1bd8aae7cb5dbeed7", + "tabbable": null, + "tooltip": null, + "value": 665.0 } }, - "93a4c1dfcfc44589926437f1ffdd3a85": { + "70176e26d6ca48bda5d33005335369ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2738,7 +2680,7 @@ "width": null } }, - "9692da0a0ad949f39b93867a9112ab58": { + "723beeec471a4b999f642afe6f412d4f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2753,33 +2695,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d4493c8ca6d74012b8cda7556ffcfcbb", + "layout": "IPY_MODEL_0a9bbe4ae4a7460aa182a6b390e2126a", "placeholder": "​", - "style": "IPY_MODEL_154d34bbb18c4e6bb7471d21431c8407", + "style": "IPY_MODEL_617bb9dddcf14ecb9bae9c187156bb6c", "tabbable": null, "tooltip": null, - "value": " 232k/232k [00:00<00:00, 3.64MB/s]" - } - }, - "96ad5243a28540c1bbd13701050cd8c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": "tokenizer.json: 100%" } }, - "98a1dc889ed543ddb76c46e918f80a38": { + "723f6f685bea42f196189855c383ef77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2794,15 +2718,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2fa841989e734ca59ab3392a1c472375", + "layout": "IPY_MODEL_7b295c74e39947a6b06414e1b23aef24", "placeholder": "​", - "style": "IPY_MODEL_73e88da4acc94040992fd88d0a0d19ed", + "style": "IPY_MODEL_6b9cbd8a34764f13b850393a56c2c764", "tabbable": null, "tooltip": null, - "value": "tokenizer_config.json: 100%" + "value": " 2.21k/2.21k [00:00<00:00, 409kB/s]" + } + }, + "77b2da3b157a4e658101eddffc7a0c02": { + "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 } }, - "99072ca746ea485cbb170400eb0e5a45": { + "7b295c74e39947a6b06414e1b23aef24": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2855,23 +2832,31 @@ "width": null } }, - "9c60afb1733442418ab367430bbb0d68": { + "7bf569b1ec4240fbb7f1457722fe46c9": { "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_f585c8f15e5c4595a88ccd3bd0205013", + "IPY_MODEL_9116b550551046e580f470834f6b5b3a", + "IPY_MODEL_b46d0225e69b453dac54123c7dfe9aa9" + ], + "layout": "IPY_MODEL_16a4915303e24ee999326b95f314a277", + "tabbable": null, + "tooltip": null } }, - "aa7d45a4b72346af98fb57bd52e6b237": { + "7d0ebaac4f0b43a498272ea2a11b15a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2924,67 +2909,134 @@ "width": null } }, - "ae26881588fc4f9695b5cbd0549eb30a": { + "8944d62a93fe49f991ff8bacfeac322c": { "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": "" } }, - "af41f929d6ed42e8b6fa4fd762ea4ebe": { + "8ad231daa0c44bdbabf3c3cc6a0548db": { "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_3e46cd86409a4a129a6d5d33c5d96e40", + "placeholder": "​", + "style": "IPY_MODEL_69666c8921454e55aebdd3cb6ea39a73", + "tabbable": null, + "tooltip": null, + "value": "README.md: 100%" + } + }, + "9116b550551046e580f470834f6b5b3a": { + "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_e5f98261e33e4e68983283a794b3ce3e", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9c07f5c15bae4ce8b5469d47f4fedca6", + "tabbable": null, + "tooltip": null, + "value": 391.0 + } + }, + "9c07f5c15bae4ce8b5469d47f4fedca6": { + "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": "" } }, - "b4b2323ffd9349f1ad2d4d50a0288dc5": { + "a8240a8e7877465086dc27694c472f92": { "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_65182ed5c6764915b44ed26f3452c6e8", - "IPY_MODEL_84060f74615349bd9e7f70b839d37c3e", - "IPY_MODEL_b76df53d2353433cb2fea0cde0c2d1dd" - ], - "layout": "IPY_MODEL_626cdb8c3d374c168811bd920a5a68f8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_70176e26d6ca48bda5d33005335369ce", + "placeholder": "​", + "style": "IPY_MODEL_f8e1af21c2314931b28a2ffdccd38cc1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 9.68kB/s]" + } + }, + "adcd6fe3722a404cbb4578c43dfb8622": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_bfef1d0d61ca4f1f8e1818ed08702bea", + "placeholder": "​", + "style": "IPY_MODEL_ec66181f622246e6b126cf01ee0e0c1f", + "tabbable": null, + "tooltip": null, + "value": "config.json: 100%" } }, - "b76df53d2353433cb2fea0cde0c2d1dd": { + "b46d0225e69b453dac54123c7dfe9aa9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2999,15 +3051,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4cb28f8d4c2846bba02492de8371ef86", + "layout": "IPY_MODEL_4e78d34c35c64a8ba9e42340cface541", "placeholder": "​", - "style": "IPY_MODEL_673761d4701d4ee5aaabc0e22e4ec6cb", + "style": "IPY_MODEL_5d9fbb50ea574e9f80a04d65984ec3fb", "tabbable": null, "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 206MB/s]" + "value": " 391/391 [00:00<00:00, 62.9kB/s]" + } + }, + "bfef1d0d61ca4f1f8e1818ed08702bea": { + "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 } }, - "b8283fed28004000b84e2f53babadac1": { + "c1eada512714499a9689453bf4dd17bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3060,71 +3165,99 @@ "width": null } }, - "b917898a95b64706aca98aba5a2b9969": { + "c550a7da6dee4658a5e958b278220075": { + "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_2b51658eec434f6c8284bd799690104b", + "IPY_MODEL_d117d190931044b78c7d6506f9a291f8", + "IPY_MODEL_12a2c923802042bbaf7afcb489317fc2" + ], + "layout": "IPY_MODEL_2b64f899a53543fb8a6355d358ad40dc", + "tabbable": null, + "tooltip": null + } + }, + "cf82eb1ddad343378d886f2d760e8e19": { "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_846cc1a5fbd9438da4609b439141f308", - "placeholder": "​", - "style": "IPY_MODEL_af41f929d6ed42e8b6fa4fd762ea4ebe", - "tabbable": null, - "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 314kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bc14afac06eb4c26bf5c7c100334328e": { + "d117d190931044b78c7d6506f9a291f8": { "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_10da9ba1022e498b8f5f3f98c7898223", - "placeholder": "​", - "style": "IPY_MODEL_34ddf006f73a4753b211a5999ec0d671", + "layout": "IPY_MODEL_40e898ed62284e21bf36ba199efb74ca", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_45bfcc5c5c3e4a10b411f71fb3237d72", "tabbable": null, "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.65kB/s]" + "value": 231508.0 } }, - "c4c2ce7cae784b2093ba53e3609cc2c9": { + "dc4eb1dc64da457a9d83b0bad4f4fd96": { "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_8ad231daa0c44bdbabf3c3cc6a0548db", + "IPY_MODEL_3127352d799744d391cc01252a89de9a", + "IPY_MODEL_723f6f685bea42f196189855c383ef77" + ], + "layout": "IPY_MODEL_e996d9e989544a01a9db1b3ba3e4bdb6", + "tabbable": null, + "tooltip": null } }, - "c634f1241a9e40659957b6a8dd57b66b": { + "e06dfc4b5e33464b8b271badc88425c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3177,7 +3310,7 @@ "width": null } }, - "c876ccec48d84a04bb874ff6b48f8030": { + "e5f98261e33e4e68983283a794b3ce3e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3230,7 +3363,7 @@ "width": null } }, - "d4493c8ca6d74012b8cda7556ffcfcbb": { + "e996d9e989544a01a9db1b3ba3e4bdb6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3283,30 +3416,43 @@ "width": null } }, - "dc79b458bbac454fbab119272509e252": { + "ec66181f622246e6b126cf01ee0e0c1f": { "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_99072ca746ea485cbb170400eb0e5a45", - "placeholder": "​", - "style": "IPY_MODEL_ae26881588fc4f9695b5cbd0549eb30a", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 56.2kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "eca51dd4d6224552b6d374e90dd7f129": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "dd603ce908534d83a9e5812536cbaecf": { + "ecf47058d5624d17a0cd5595cc9a9ea1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3359,57 +3505,25 @@ "width": null } }, - "e6b938e7ce354e6ebb9c5105fe3bde01": { - "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_98a1dc889ed543ddb76c46e918f80a38", - "IPY_MODEL_fbbd836ae0ab4f63bed278c2565cd3f1", - "IPY_MODEL_bc14afac06eb4c26bf5c7c100334328e" - ], - "layout": "IPY_MODEL_aa7d45a4b72346af98fb57bd52e6b237", - "tabbable": null, - "tooltip": null - } - }, - "ea5aad74db984381a9502d15f7877dc9": { + "f350410541a947db9efae73d4e6d000d": { "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_5f624de673e9405cb01619e550cd02b5", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_62cc1ededdbc4ebaa9a9455fc402d06e", - "tabbable": null, - "tooltip": null, - "value": 2211.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "efcccd1f66e4459cb1a7709eadb26866": { + "f585c8f15e5c4595a88ccd3bd0205013": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3424,15 +3538,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f94845de837b4883b0f99fb9f3b7ead2", + "layout": "IPY_MODEL_17d77342577f4828b221978f61b1cca1", "placeholder": "​", - "style": "IPY_MODEL_27cccac5127445d09232f53a08657063", + "style": "IPY_MODEL_03473efde97f4db7aeb4bf94a8dcb6ac", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 124kB/s]" + "value": ".gitattributes: 100%" } }, - "f6577867a1c041f490b073388151f1be": { + "f85452b3a7ee4bd8b96746e8f9cbd2a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3447,121 +3561,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_89c1af10a3bf46378b2d5ad1570f4844", + "layout": "IPY_MODEL_09d4dbd7007b4cfda79c92844565bfd3", "placeholder": "​", - "style": "IPY_MODEL_390080de929440ada6a97f2e0d2dc60f", + "style": "IPY_MODEL_eca51dd4d6224552b6d374e90dd7f129", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" - } - }, - "f7f7d73c85274703989c1a758316f306": { - "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": "tokenizer_config.json: 100%" } }, - "f94845de837b4883b0f99fb9f3b7ead2": { - "model_module": "@jupyter-widgets/base", + "f8e1af21c2314931b28a2ffdccd38cc1": { + "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 } }, - "fa4036e56d43472283e556f828cf84fd": { + "fb13c0f13bd34a04a46ba3462d87a3cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3613,32 +3639,6 @@ "visibility": null, "width": null } - }, - "fbbd836ae0ab4f63bed278c2565cd3f1": { - "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_1b4847f4ce0741c391f613e88b131aaa", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5bed445f56a545b89b799f73d2462bd9", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index f932534b5..5e61abbb0 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1351,7 +1351,7 @@

5. Use cleanlab to find label issues -{"state": {"0e3467cf59954459ab486aee2ba9c3a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6c036dfeb50042c8984a6a6692fc0f9b": {"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": ""}}, "318a448eedbf4c68b4b67978734a1ae9": {"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_0e3467cf59954459ab486aee2ba9c3a5", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6c036dfeb50042c8984a6a6692fc0f9b", "tabbable": null, "tooltip": null, "value": 2041.0}}, "700bb6482b2c4111b4ed9390c8470861": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f9b3a44be9f34f6e93de758ee46b92ec": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ef0ba41ae31d4fca866f944d42378821": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_700bb6482b2c4111b4ed9390c8470861", "placeholder": "\u200b", "style": "IPY_MODEL_f9b3a44be9f34f6e93de758ee46b92ec", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "2741fbda14764533a6d7865887e84821": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "132a82bf8c9844f59e250a9598747c76": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6fc0bc69349045329fcb28a46a2fe14b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2741fbda14764533a6d7865887e84821", "placeholder": "\u200b", "style": "IPY_MODEL_132a82bf8c9844f59e250a9598747c76", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007482kB/s]"}}, "2a2e0134b1234019be47ad459a2c7e6e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "53dafb78e671406e89f3754d23b34684": {"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_ef0ba41ae31d4fca866f944d42378821", "IPY_MODEL_318a448eedbf4c68b4b67978734a1ae9", "IPY_MODEL_6fc0bc69349045329fcb28a46a2fe14b"], "layout": "IPY_MODEL_2a2e0134b1234019be47ad459a2c7e6e", "tabbable": null, "tooltip": null}}, "8b0da1e1f92449e49814a4792b3a0a18": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fa5d63111e8040c8a46a0ef606a7b541": {"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": ""}}, "9a1d64682df846808b68e96652571971": {"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_8b0da1e1f92449e49814a4792b3a0a18", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fa5d63111e8040c8a46a0ef606a7b541", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "f948f7fae2bd4b23b89c1a9f86de6cdc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0b18f93966c84db6bd40967285652faf": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6a89c26dda78427781977305cd34e44b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f948f7fae2bd4b23b89c1a9f86de6cdc", "placeholder": "\u200b", "style": "IPY_MODEL_0b18f93966c84db6bd40967285652faf", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "d563179a4e534dd4b15465aa4a240b93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0dd17ee1b414e0dbba6940c708a7553": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "08327f8f533f49bb8518d3413af11e27": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d563179a4e534dd4b15465aa4a240b93", "placeholder": "\u200b", "style": "IPY_MODEL_c0dd17ee1b414e0dbba6940c708a7553", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007169MB/s]"}}, "819ab4db9b9249cfb05199eec4ffe2ae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e4e9c1fa715d49009ec1097cf561d5f6": {"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_6a89c26dda78427781977305cd34e44b", "IPY_MODEL_9a1d64682df846808b68e96652571971", "IPY_MODEL_08327f8f533f49bb8518d3413af11e27"], "layout": "IPY_MODEL_819ab4db9b9249cfb05199eec4ffe2ae", "tabbable": null, "tooltip": null}}, "7ba99fd178de4f25867b6e37949a6d85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f321f0e3281b4b478dffcfb404472020": {"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": ""}}, "5aded6b96abc4bfebf8a38a3dcad2d6f": {"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_7ba99fd178de4f25867b6e37949a6d85", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f321f0e3281b4b478dffcfb404472020", "tabbable": null, "tooltip": null, "value": 3201.0}}, "71c1b39c84cf47b5a477e26da271fef9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7ca38434b4e44b5b86b173c7855b4ff3": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b4b3cc9f4e4c408998a1c0ebec86d5bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_71c1b39c84cf47b5a477e26da271fef9", "placeholder": "\u200b", "style": "IPY_MODEL_7ca38434b4e44b5b86b173c7855b4ff3", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "46e0a3db3d4b44aea458c9d15b1b45b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b420c63e4f174bba8f48175457a23dfe": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4c2a6bff30b84735af3cf83ab6de7ddf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_46e0a3db3d4b44aea458c9d15b1b45b8", "placeholder": "\u200b", "style": "IPY_MODEL_b420c63e4f174bba8f48175457a23dfe", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007804kB/s]"}}, "6eec35a271e4402b8f28109615706306": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5ee4ab99d955426daf559df8bf71c44f": {"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_b4b3cc9f4e4c408998a1c0ebec86d5bc", "IPY_MODEL_5aded6b96abc4bfebf8a38a3dcad2d6f", "IPY_MODEL_4c2a6bff30b84735af3cf83ab6de7ddf"], "layout": "IPY_MODEL_6eec35a271e4402b8f28109615706306", "tabbable": null, "tooltip": null}}, "c920632573cc4fe0976140070f9677ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b97eebf068ca4e9c85cbadbb4cc103c4": {"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": ""}}, "31da699ce27d4748a3b0908daaeff226": {"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_c920632573cc4fe0976140070f9677ec", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b97eebf068ca4e9c85cbadbb4cc103c4", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "dd83345f7f5c46a38920bb555c0f6b9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0dc2781328864c55a124d3ba0119a934": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "30eda8d6d1724ca0a98cb1391ef57cf4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_dd83345f7f5c46a38920bb555c0f6b9a", "placeholder": "\u200b", "style": "IPY_MODEL_0dc2781328864c55a124d3ba0119a934", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "4c6822f78a694818a65c111aed9cd2c2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3c6b0d1ab4745c796d69b14cb880bf5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "c979e50a880a49aeaf6e580f1f3ff7e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4c6822f78a694818a65c111aed9cd2c2", "placeholder": "\u200b", "style": "IPY_MODEL_e3c6b0d1ab4745c796d69b14cb880bf5", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007212MB/s]"}}, "e41b0c6637564566b921cc1987c4bf9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2a4a612a6d2846bca788bddf1043cc09": {"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_30eda8d6d1724ca0a98cb1391ef57cf4", "IPY_MODEL_31da699ce27d4748a3b0908daaeff226", "IPY_MODEL_c979e50a880a49aeaf6e580f1f3ff7e8"], "layout": "IPY_MODEL_e41b0c6637564566b921cc1987c4bf9a", "tabbable": null, "tooltip": null}}, "9f5000cf7d6a4079b14d5f3c666d8d9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c12a918e266b40caa2ad3eb5ba27297c": {"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": ""}}, "15a248e8576b4e1cace7306d79423606": {"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_9f5000cf7d6a4079b14d5f3c666d8d9a", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c12a918e266b40caa2ad3eb5ba27297c", "tabbable": null, "tooltip": null, "value": 128619.0}}, "701065566fa2417290be013d76599838": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "798cb5dd7eb545d5a4188e416dce6f88": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a79c4f7046b44587aab8c79e98299302": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_701065566fa2417290be013d76599838", "placeholder": "\u200b", "style": "IPY_MODEL_798cb5dd7eb545d5a4188e416dce6f88", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "10945e8601a446f2bb59fa1211f86f5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "214135184cdb4c61851d09d4776b8681": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "de328081cf5143bda2f18a1f732277b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_10945e8601a446f2bb59fa1211f86f5b", "placeholder": "\u200b", "style": "IPY_MODEL_214135184cdb4c61851d09d4776b8681", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200724.4MB/s]"}}, "b8e950254c7c4bfc904715885aa32fa1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f814d21bb5204a479acad09d629678fa": {"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_a79c4f7046b44587aab8c79e98299302", "IPY_MODEL_15a248e8576b4e1cace7306d79423606", "IPY_MODEL_de328081cf5143bda2f18a1f732277b0"], "layout": "IPY_MODEL_b8e950254c7c4bfc904715885aa32fa1", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"2d3c1dc1060c4165802a43d8a1506254": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "946fc47d18224c0287afaa056d02fa86": {"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": ""}}, "c26558f6f8394f7483fa04455db2ead3": {"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_2d3c1dc1060c4165802a43d8a1506254", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_946fc47d18224c0287afaa056d02fa86", "tabbable": null, "tooltip": null, "value": 2041.0}}, "419fb750a9174f9dbee80f1946cac5e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "04992c51b0da458e880d33ce9b344519": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fdc61f1ca4794f988ad26aa36f51cded": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_419fb750a9174f9dbee80f1946cac5e2", "placeholder": "\u200b", "style": "IPY_MODEL_04992c51b0da458e880d33ce9b344519", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "59de9bdd6b4e4c99946071c77765c06e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a77c2cc33dd84816aca2750dfa77985f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "34e4112c8e594ad7811823c85546de7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_59de9bdd6b4e4c99946071c77765c06e", "placeholder": "\u200b", "style": "IPY_MODEL_a77c2cc33dd84816aca2750dfa77985f", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007485kB/s]"}}, "da43246f2ddc4df9968898f36b1bfc0c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "86394b0091ab4767834e877eb97c7f29": {"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_fdc61f1ca4794f988ad26aa36f51cded", "IPY_MODEL_c26558f6f8394f7483fa04455db2ead3", "IPY_MODEL_34e4112c8e594ad7811823c85546de7c"], "layout": "IPY_MODEL_da43246f2ddc4df9968898f36b1bfc0c", "tabbable": null, "tooltip": null}}, "e7fc281836224420a165582ad5a1f00e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "367f32394edb4f978af0778e4a33a113": {"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": ""}}, "54fd655fb2ea4e58ba287b8a699358a4": {"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_e7fc281836224420a165582ad5a1f00e", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_367f32394edb4f978af0778e4a33a113", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "7f6ff60608894304be071471768422d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "89ca8bee4d29420391f60780a4e5bb37": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e83d24db49ae4489ac3ce5c785ee2c56": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7f6ff60608894304be071471768422d5", "placeholder": "\u200b", "style": "IPY_MODEL_89ca8bee4d29420391f60780a4e5bb37", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "a7073edae4e4487fb17ec295efaf2195": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "62b1b0b90a684d3488af171459b2e05b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fb154ca6183045b2bcadfc5b92658f12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a7073edae4e4487fb17ec295efaf2195", "placeholder": "\u200b", "style": "IPY_MODEL_62b1b0b90a684d3488af171459b2e05b", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200756.5MB/s]"}}, "5704c0dc0c934dab903a45047e08bfa1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aea522f486884287934824875f65568c": {"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_e83d24db49ae4489ac3ce5c785ee2c56", "IPY_MODEL_54fd655fb2ea4e58ba287b8a699358a4", "IPY_MODEL_fb154ca6183045b2bcadfc5b92658f12"], "layout": "IPY_MODEL_5704c0dc0c934dab903a45047e08bfa1", "tabbable": null, "tooltip": null}}, "b67e2b3c22e243868a621e764d7615bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3cf8422c429b489fa3609423681b6f05": {"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": ""}}, "99c304eddb9d4f808e1444eda066b707": {"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_b67e2b3c22e243868a621e764d7615bd", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3cf8422c429b489fa3609423681b6f05", "tabbable": null, "tooltip": null, "value": 3201.0}}, "f841644ac2b04256b2f3c4ad9a4db82f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "67269dc6c7a64c2a85d31a7de8c68e5a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "78baba5e6db44fa6a19c704bf00fb60c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f841644ac2b04256b2f3c4ad9a4db82f", "placeholder": "\u200b", "style": "IPY_MODEL_67269dc6c7a64c2a85d31a7de8c68e5a", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "8c24d36de7d04ba8a98dbc6243bffa1e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c4f6d1fdbf7f4c11b3b58973c52c1d0d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "be4a812783c5484dbde2feef6424ebe3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8c24d36de7d04ba8a98dbc6243bffa1e", "placeholder": "\u200b", "style": "IPY_MODEL_c4f6d1fdbf7f4c11b3b58973c52c1d0d", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007792kB/s]"}}, "9198ec1e4cbc45a5bbf7c1f0e8976f79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9001078dd11947c3b743e880d41d89e0": {"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_78baba5e6db44fa6a19c704bf00fb60c", "IPY_MODEL_99c304eddb9d4f808e1444eda066b707", "IPY_MODEL_be4a812783c5484dbde2feef6424ebe3"], "layout": "IPY_MODEL_9198ec1e4cbc45a5bbf7c1f0e8976f79", "tabbable": null, "tooltip": null}}, "73e424d16cbf4e40ad3206856f075d5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2e538bcb8e2a4e88a9d22def0a5b4c06": {"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": ""}}, "db99e931b4c94d2fb4385e2ccfaa8522": {"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_73e424d16cbf4e40ad3206856f075d5b", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2e538bcb8e2a4e88a9d22def0a5b4c06", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "d12f8fa702da44f6b81f1b84c1c76383": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6cd21c0f3c7d4b668b81735903eedb1b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6aef8917b0084643af4ba12c5101c4a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d12f8fa702da44f6b81f1b84c1c76383", "placeholder": "\u200b", "style": "IPY_MODEL_6cd21c0f3c7d4b668b81735903eedb1b", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "b831f552387747248fdc25346996cacd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "78c7d82d53664b95a386ec96a27b6453": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "21a62ae51e6d4cf38d4e04afa3c63d08": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b831f552387747248fdc25346996cacd", "placeholder": "\u200b", "style": "IPY_MODEL_78c7d82d53664b95a386ec96a27b6453", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200775.8MB/s]"}}, "355467884efe4ca683c9294faddd8e67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4187151778314617bdd65072c0c39e18": {"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_6aef8917b0084643af4ba12c5101c4a0", "IPY_MODEL_db99e931b4c94d2fb4385e2ccfaa8522", "IPY_MODEL_21a62ae51e6d4cf38d4e04afa3c63d08"], "layout": "IPY_MODEL_355467884efe4ca683c9294faddd8e67", "tabbable": null, "tooltip": null}}, "6801e67ee9a2448e90812fa8fea0d356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c9789735e7aa44fb8c6bcf3f8ceeebf2": {"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": ""}}, "4a0472fddab34db383019c14f53513c2": {"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_6801e67ee9a2448e90812fa8fea0d356", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c9789735e7aa44fb8c6bcf3f8ceeebf2", "tabbable": null, "tooltip": null, "value": 128619.0}}, "73080f2ef1784e73947c31dfebce4e08": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fe5e77a095e54cad8e7f53fae0f07637": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "99009d85bc164733af718beec07a0dd3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_73080f2ef1784e73947c31dfebce4e08", "placeholder": "\u200b", "style": "IPY_MODEL_fe5e77a095e54cad8e7f53fae0f07637", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "0ef4fd968d284787a6d3621ab522b0f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fb8ea71a45ce49d1bc0bf64d3de435a1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "467e4df618db47e2aa4db7b4eeb1b07e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0ef4fd968d284787a6d3621ab522b0f4", "placeholder": "\u200b", "style": "IPY_MODEL_fb8ea71a45ce49d1bc0bf64d3de435a1", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20075.74MB/s]"}}, "14d064897b934c9fa649860e4a0d136f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7aac7a5b86074e509ae85dca28a96569": {"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_99009d85bc164733af718beec07a0dd3", "IPY_MODEL_4a0472fddab34db383019c14f53513c2", "IPY_MODEL_467e4df618db47e2aa4db7b4eeb1b07e"], "layout": "IPY_MODEL_14d064897b934c9fa649860e4a0d136f", "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 29bf50217..25d6d5a74 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": "2024-09-06T19:33:15.412497Z", - "iopub.status.busy": "2024-09-06T19:33:15.412315Z", - "iopub.status.idle": "2024-09-06T19:33:20.744505Z", - "shell.execute_reply": "2024-09-06T19:33:20.743930Z" + "iopub.execute_input": "2024-09-26T14:47:15.535813Z", + "iopub.status.busy": "2024-09-26T14:47:15.535636Z", + "iopub.status.idle": "2024-09-26T14:47:21.234346Z", + "shell.execute_reply": "2024-09-26T14:47:21.233674Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:20.747320Z", - "iopub.status.busy": "2024-09-06T19:33:20.746730Z", - "iopub.status.idle": "2024-09-06T19:33:20.750172Z", - "shell.execute_reply": "2024-09-06T19:33:20.749624Z" + "iopub.execute_input": "2024-09-26T14:47:21.236766Z", + "iopub.status.busy": "2024-09-26T14:47:21.236377Z", + "iopub.status.idle": "2024-09-26T14:47:21.239643Z", + "shell.execute_reply": "2024-09-26T14:47:21.239185Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:20.752397Z", - "iopub.status.busy": "2024-09-06T19:33:20.751947Z", - "iopub.status.idle": "2024-09-06T19:33:20.756917Z", - "shell.execute_reply": "2024-09-06T19:33:20.756445Z" + "iopub.execute_input": "2024-09-26T14:47:21.241324Z", + "iopub.status.busy": "2024-09-26T14:47:21.240999Z", + "iopub.status.idle": "2024-09-26T14:47:21.245772Z", + "shell.execute_reply": "2024-09-26T14:47:21.245316Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:20.758862Z", - "iopub.status.busy": "2024-09-06T19:33:20.758684Z", - "iopub.status.idle": "2024-09-06T19:33:22.662809Z", - "shell.execute_reply": "2024-09-06T19:33:22.662142Z" + "iopub.execute_input": "2024-09-26T14:47:21.247572Z", + "iopub.status.busy": "2024-09-26T14:47:21.247248Z", + "iopub.status.idle": "2024-09-26T14:47:23.090757Z", + "shell.execute_reply": "2024-09-26T14:47:23.089916Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.665411Z", - "iopub.status.busy": "2024-09-06T19:33:22.665209Z", - "iopub.status.idle": "2024-09-06T19:33:22.675958Z", - "shell.execute_reply": "2024-09-06T19:33:22.675514Z" + "iopub.execute_input": "2024-09-26T14:47:23.093045Z", + "iopub.status.busy": "2024-09-26T14:47:23.092832Z", + "iopub.status.idle": "2024-09-26T14:47:23.103750Z", + "shell.execute_reply": "2024-09-26T14:47:23.103275Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.677986Z", - "iopub.status.busy": "2024-09-06T19:33:22.677801Z", - "iopub.status.idle": "2024-09-06T19:33:22.684956Z", - "shell.execute_reply": "2024-09-06T19:33:22.684474Z" + "iopub.execute_input": "2024-09-26T14:47:23.105636Z", + "iopub.status.busy": "2024-09-26T14:47:23.105213Z", + "iopub.status.idle": "2024-09-26T14:47:23.110875Z", + "shell.execute_reply": "2024-09-26T14:47:23.110416Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:22.686790Z", - "iopub.status.busy": "2024-09-06T19:33:22.686606Z", - "iopub.status.idle": "2024-09-06T19:33:23.132191Z", - "shell.execute_reply": "2024-09-06T19:33:23.131660Z" + "iopub.execute_input": "2024-09-26T14:47:23.112413Z", + "iopub.status.busy": "2024-09-26T14:47:23.112235Z", + "iopub.status.idle": "2024-09-26T14:47:23.593390Z", + "shell.execute_reply": "2024-09-26T14:47:23.592868Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:23.134446Z", - "iopub.status.busy": "2024-09-06T19:33:23.134077Z", - "iopub.status.idle": "2024-09-06T19:33:24.169658Z", - "shell.execute_reply": "2024-09-06T19:33:24.169048Z" + "iopub.execute_input": "2024-09-26T14:47:23.595287Z", + "iopub.status.busy": "2024-09-26T14:47:23.594919Z", + "iopub.status.idle": "2024-09-26T14:47:24.771320Z", + "shell.execute_reply": "2024-09-26T14:47:24.770797Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.172059Z", - "iopub.status.busy": "2024-09-06T19:33:24.171874Z", - "iopub.status.idle": "2024-09-06T19:33:24.191001Z", - "shell.execute_reply": "2024-09-06T19:33:24.190537Z" + "iopub.execute_input": "2024-09-26T14:47:24.773502Z", + "iopub.status.busy": "2024-09-26T14:47:24.773132Z", + "iopub.status.idle": "2024-09-26T14:47:24.791888Z", + "shell.execute_reply": "2024-09-26T14:47:24.791419Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.193080Z", - "iopub.status.busy": "2024-09-06T19:33:24.192898Z", - "iopub.status.idle": "2024-09-06T19:33:24.196091Z", - "shell.execute_reply": "2024-09-06T19:33:24.195633Z" + "iopub.execute_input": "2024-09-26T14:47:24.793633Z", + "iopub.status.busy": "2024-09-26T14:47:24.793274Z", + "iopub.status.idle": "2024-09-26T14:47:24.796512Z", + "shell.execute_reply": "2024-09-26T14:47:24.796062Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:24.198156Z", - "iopub.status.busy": "2024-09-06T19:33:24.197822Z", - "iopub.status.idle": "2024-09-06T19:33:38.175563Z", - "shell.execute_reply": "2024-09-06T19:33:38.174995Z" + "iopub.execute_input": "2024-09-26T14:47:24.798106Z", + "iopub.status.busy": "2024-09-26T14:47:24.797929Z", + "iopub.status.idle": "2024-09-26T14:47:39.925585Z", + "shell.execute_reply": "2024-09-26T14:47:39.924902Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.178313Z", - "iopub.status.busy": "2024-09-06T19:33:38.177918Z", - "iopub.status.idle": "2024-09-06T19:33:38.181776Z", - "shell.execute_reply": "2024-09-06T19:33:38.181209Z" + "iopub.execute_input": "2024-09-26T14:47:39.927963Z", + "iopub.status.busy": "2024-09-26T14:47:39.927564Z", + "iopub.status.idle": "2024-09-26T14:47:39.931572Z", + "shell.execute_reply": "2024-09-26T14:47:39.931070Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.183755Z", - "iopub.status.busy": "2024-09-06T19:33:38.183579Z", - "iopub.status.idle": "2024-09-06T19:33:38.879592Z", - "shell.execute_reply": "2024-09-06T19:33:38.878973Z" + "iopub.execute_input": "2024-09-26T14:47:39.933402Z", + "iopub.status.busy": "2024-09-26T14:47:39.933168Z", + "iopub.status.idle": "2024-09-26T14:47:40.683130Z", + "shell.execute_reply": "2024-09-26T14:47:40.682533Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.882730Z", - "iopub.status.busy": "2024-09-06T19:33:38.882295Z", - "iopub.status.idle": "2024-09-06T19:33:38.887349Z", - "shell.execute_reply": "2024-09-06T19:33:38.886834Z" + "iopub.execute_input": "2024-09-26T14:47:40.685556Z", + "iopub.status.busy": "2024-09-26T14:47:40.684983Z", + "iopub.status.idle": "2024-09-26T14:47:40.690268Z", + "shell.execute_reply": "2024-09-26T14:47:40.689730Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.889963Z", - "iopub.status.busy": "2024-09-06T19:33:38.889560Z", - "iopub.status.idle": "2024-09-06T19:33:38.996371Z", - "shell.execute_reply": "2024-09-06T19:33:38.995754Z" + "iopub.execute_input": "2024-09-26T14:47:40.693275Z", + "iopub.status.busy": "2024-09-26T14:47:40.692350Z", + "iopub.status.idle": "2024-09-26T14:47:40.818191Z", + "shell.execute_reply": "2024-09-26T14:47:40.817594Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:38.998935Z", - "iopub.status.busy": "2024-09-06T19:33:38.998516Z", - "iopub.status.idle": "2024-09-06T19:33:39.011487Z", - "shell.execute_reply": "2024-09-06T19:33:39.010948Z" + "iopub.execute_input": "2024-09-26T14:47:40.820305Z", + "iopub.status.busy": "2024-09-26T14:47:40.819910Z", + "iopub.status.idle": "2024-09-26T14:47:40.832452Z", + "shell.execute_reply": "2024-09-26T14:47:40.831961Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.013725Z", - "iopub.status.busy": "2024-09-06T19:33:39.013368Z", - "iopub.status.idle": "2024-09-06T19:33:39.021505Z", - "shell.execute_reply": "2024-09-06T19:33:39.020914Z" + "iopub.execute_input": "2024-09-26T14:47:40.834307Z", + "iopub.status.busy": "2024-09-26T14:47:40.833985Z", + "iopub.status.idle": "2024-09-26T14:47:40.842566Z", + "shell.execute_reply": "2024-09-26T14:47:40.842095Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.023705Z", - "iopub.status.busy": "2024-09-06T19:33:39.023358Z", - "iopub.status.idle": "2024-09-06T19:33:39.027633Z", - "shell.execute_reply": "2024-09-06T19:33:39.027085Z" + "iopub.execute_input": "2024-09-26T14:47:40.844168Z", + "iopub.status.busy": "2024-09-26T14:47:40.843976Z", + "iopub.status.idle": "2024-09-26T14:47:40.848543Z", + "shell.execute_reply": "2024-09-26T14:47:40.847986Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.029757Z", - "iopub.status.busy": "2024-09-06T19:33:39.029380Z", - "iopub.status.idle": "2024-09-06T19:33:39.035357Z", - "shell.execute_reply": "2024-09-06T19:33:39.034867Z" + "iopub.execute_input": "2024-09-26T14:47:40.850235Z", + "iopub.status.busy": "2024-09-26T14:47:40.850049Z", + "iopub.status.idle": "2024-09-26T14:47:40.856001Z", + "shell.execute_reply": "2024-09-26T14:47:40.855443Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.037583Z", - "iopub.status.busy": "2024-09-06T19:33:39.037235Z", - "iopub.status.idle": "2024-09-06T19:33:39.148961Z", - "shell.execute_reply": "2024-09-06T19:33:39.148428Z" + "iopub.execute_input": "2024-09-26T14:47:40.857876Z", + "iopub.status.busy": "2024-09-26T14:47:40.857600Z", + "iopub.status.idle": "2024-09-26T14:47:40.971332Z", + "shell.execute_reply": "2024-09-26T14:47:40.970730Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.151080Z", - "iopub.status.busy": "2024-09-06T19:33:39.150801Z", - "iopub.status.idle": "2024-09-06T19:33:39.254384Z", - "shell.execute_reply": "2024-09-06T19:33:39.253890Z" + "iopub.execute_input": "2024-09-26T14:47:40.973220Z", + "iopub.status.busy": "2024-09-26T14:47:40.972870Z", + "iopub.status.idle": "2024-09-26T14:47:41.080277Z", + "shell.execute_reply": "2024-09-26T14:47:41.079768Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.256524Z", - "iopub.status.busy": "2024-09-06T19:33:39.256169Z", - "iopub.status.idle": "2024-09-06T19:33:39.357567Z", - "shell.execute_reply": "2024-09-06T19:33:39.356999Z" + "iopub.execute_input": "2024-09-26T14:47:41.082132Z", + "iopub.status.busy": "2024-09-26T14:47:41.081755Z", + "iopub.status.idle": "2024-09-26T14:47:41.187047Z", + "shell.execute_reply": "2024-09-26T14:47:41.186551Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.359754Z", - "iopub.status.busy": "2024-09-06T19:33:39.359388Z", - "iopub.status.idle": "2024-09-06T19:33:39.459179Z", - "shell.execute_reply": "2024-09-06T19:33:39.458626Z" + "iopub.execute_input": "2024-09-26T14:47:41.188688Z", + "iopub.status.busy": "2024-09-26T14:47:41.188508Z", + "iopub.status.idle": "2024-09-26T14:47:41.292182Z", + "shell.execute_reply": "2024-09-26T14:47:41.291705Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:39.461397Z", - "iopub.status.busy": "2024-09-06T19:33:39.461068Z", - "iopub.status.idle": "2024-09-06T19:33:39.464273Z", - "shell.execute_reply": "2024-09-06T19:33:39.463742Z" + "iopub.execute_input": "2024-09-26T14:47:41.294034Z", + "iopub.status.busy": "2024-09-26T14:47:41.293720Z", + "iopub.status.idle": "2024-09-26T14:47:41.297083Z", + "shell.execute_reply": "2024-09-26T14:47:41.296508Z" }, "nbsphinx": "hidden" }, @@ -1387,53 +1387,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "08327f8f533f49bb8518d3413af11e27": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d563179a4e534dd4b15465aa4a240b93", - "placeholder": "​", - "style": "IPY_MODEL_c0dd17ee1b414e0dbba6940c708a7553", - "tabbable": null, - "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 169MB/s]" - } - }, - "0b18f93966c84db6bd40967285652faf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0dc2781328864c55a124d3ba0119a934": { + "04992c51b0da458e880d33ce9b344519": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1451,7 +1410,7 @@ "text_color": null } }, - "0e3467cf59954459ab486aee2ba9c3a5": { + "0ef4fd968d284787a6d3621ab522b0f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1504,7 +1463,7 @@ "width": null } }, - "10945e8601a446f2bb59fa1211f86f5b": { + "14d064897b934c9fa649860e4a0d136f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1557,69 +1516,30 @@ "width": null } }, - "132a82bf8c9844f59e250a9598747c76": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "15a248e8576b4e1cace7306d79423606": { + "21a62ae51e6d4cf38d4e04afa3c63d08": { "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_9f5000cf7d6a4079b14d5f3c666d8d9a", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c12a918e266b40caa2ad3eb5ba27297c", + "layout": "IPY_MODEL_b831f552387747248fdc25346996cacd", + "placeholder": "​", + "style": "IPY_MODEL_78c7d82d53664b95a386ec96a27b6453", "tabbable": null, "tooltip": null, - "value": 128619.0 - } - }, - "214135184cdb4c61851d09d4776b8681": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": " 15.9M/15.9M [00:00<00:00, 75.8MB/s]" } }, - "2741fbda14764533a6d7865887e84821": { + "2d3c1dc1060c4165802a43d8a1506254": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1672,7 +1592,46 @@ "width": null } }, - "2a2e0134b1234019be47ad459a2c7e6e": { + "2e538bcb8e2a4e88a9d22def0a5b4c06": { + "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": "" + } + }, + "34e4112c8e594ad7811823c85546de7c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_59de9bdd6b4e4c99946071c77765c06e", + "placeholder": "​", + "style": "IPY_MODEL_a77c2cc33dd84816aca2750dfa77985f", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 485kB/s]" + } + }, + "355467884efe4ca683c9294faddd8e67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1725,7 +1684,39 @@ "width": null } }, - "2a4a612a6d2846bca788bddf1043cc09": { + "367f32394edb4f978af0778e4a33a113": { + "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": "" + } + }, + "3cf8422c429b489fa3609423681b6f05": { + "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": "" + } + }, + "4187151778314617bdd65072c0c39e18": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1740,16 +1731,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_30eda8d6d1724ca0a98cb1391ef57cf4", - "IPY_MODEL_31da699ce27d4748a3b0908daaeff226", - "IPY_MODEL_c979e50a880a49aeaf6e580f1f3ff7e8" + "IPY_MODEL_6aef8917b0084643af4ba12c5101c4a0", + "IPY_MODEL_db99e931b4c94d2fb4385e2ccfaa8522", + "IPY_MODEL_21a62ae51e6d4cf38d4e04afa3c63d08" ], - "layout": "IPY_MODEL_e41b0c6637564566b921cc1987c4bf9a", + "layout": "IPY_MODEL_355467884efe4ca683c9294faddd8e67", "tabbable": null, "tooltip": null } }, - "30eda8d6d1724ca0a98cb1391ef57cf4": { + "419fb750a9174f9dbee80f1946cac5e2": { + "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 + } + }, + "467e4df618db47e2aa4db7b4eeb1b07e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1764,15 +1808,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dd83345f7f5c46a38920bb555c0f6b9a", + "layout": "IPY_MODEL_0ef4fd968d284787a6d3621ab522b0f4", "placeholder": "​", - "style": "IPY_MODEL_0dc2781328864c55a124d3ba0119a934", + "style": "IPY_MODEL_fb8ea71a45ce49d1bc0bf64d3de435a1", "tabbable": null, "tooltip": null, - "value": "classifier.ckpt: 100%" + "value": " 129k/129k [00:00<00:00, 5.74MB/s]" } }, - "318a448eedbf4c68b4b67978734a1ae9": { + "4a0472fddab34db383019c14f53513c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1788,17 +1832,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0e3467cf59954459ab486aee2ba9c3a5", - "max": 2041.0, + "layout": "IPY_MODEL_6801e67ee9a2448e90812fa8fea0d356", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6c036dfeb50042c8984a6a6692fc0f9b", + "style": "IPY_MODEL_c9789735e7aa44fb8c6bcf3f8ceeebf2", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": 128619.0 } }, - "31da699ce27d4748a3b0908daaeff226": { + "54fd655fb2ea4e58ba287b8a699358a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1814,17 +1858,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c920632573cc4fe0976140070f9677ec", - "max": 15856877.0, + "layout": "IPY_MODEL_e7fc281836224420a165582ad5a1f00e", + "max": 16887676.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b97eebf068ca4e9c85cbadbb4cc103c4", + "style": "IPY_MODEL_367f32394edb4f978af0778e4a33a113", "tabbable": null, "tooltip": null, - "value": 15856877.0 + "value": 16887676.0 } }, - "46e0a3db3d4b44aea458c9d15b1b45b8": { + "5704c0dc0c934dab903a45047e08bfa1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1877,30 +1921,7 @@ "width": null } }, - "4c2a6bff30b84735af3cf83ab6de7ddf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_46e0a3db3d4b44aea458c9d15b1b45b8", - "placeholder": "​", - "style": "IPY_MODEL_b420c63e4f174bba8f48175457a23dfe", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 804kB/s]" - } - }, - "4c6822f78a694818a65c111aed9cd2c2": { + "59de9bdd6b4e4c99946071c77765c06e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1953,120 +1974,43 @@ "width": null } }, - "53dafb78e671406e89f3754d23b34684": { + "62b1b0b90a684d3488af171459b2e05b": { "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_ef0ba41ae31d4fca866f944d42378821", - "IPY_MODEL_318a448eedbf4c68b4b67978734a1ae9", - "IPY_MODEL_6fc0bc69349045329fcb28a46a2fe14b" - ], - "layout": "IPY_MODEL_2a2e0134b1234019be47ad459a2c7e6e", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5aded6b96abc4bfebf8a38a3dcad2d6f": { + "67269dc6c7a64c2a85d31a7de8c68e5a": { "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_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7ba99fd178de4f25867b6e37949a6d85", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f321f0e3281b4b478dffcfb404472020", - "tabbable": null, - "tooltip": null, - "value": 3201.0 - } - }, - "5ee4ab99d955426daf559df8bf71c44f": { - "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_b4b3cc9f4e4c408998a1c0ebec86d5bc", - "IPY_MODEL_5aded6b96abc4bfebf8a38a3dcad2d6f", - "IPY_MODEL_4c2a6bff30b84735af3cf83ab6de7ddf" - ], - "layout": "IPY_MODEL_6eec35a271e4402b8f28109615706306", - "tabbable": null, - "tooltip": null - } - }, - "6a89c26dda78427781977305cd34e44b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f948f7fae2bd4b23b89c1a9f86de6cdc", - "placeholder": "​", - "style": "IPY_MODEL_0b18f93966c84db6bd40967285652faf", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "6c036dfeb50042c8984a6a6692fc0f9b": { - "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": "@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 } }, - "6eec35a271e4402b8f28109615706306": { + "6801e67ee9a2448e90812fa8fea0d356": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2119,7 +2063,7 @@ "width": null } }, - "6fc0bc69349045329fcb28a46a2fe14b": { + "6aef8917b0084643af4ba12c5101c4a0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2134,68 +2078,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_2741fbda14764533a6d7865887e84821", + "layout": "IPY_MODEL_d12f8fa702da44f6b81f1b84c1c76383", "placeholder": "​", - "style": "IPY_MODEL_132a82bf8c9844f59e250a9598747c76", + "style": "IPY_MODEL_6cd21c0f3c7d4b668b81735903eedb1b", "tabbable": null, "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 482kB/s]" + "value": "classifier.ckpt: 100%" } }, - "700bb6482b2c4111b4ed9390c8470861": { - "model_module": "@jupyter-widgets/base", + "6cd21c0f3c7d4b668b81735903eedb1b": { + "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 } }, - "701065566fa2417290be013d76599838": { + "73080f2ef1784e73947c31dfebce4e08": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2248,7 +2157,7 @@ "width": null } }, - "71c1b39c84cf47b5a477e26da271fef9": { + "73e424d16cbf4e40ad3206856f075d5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2301,7 +2210,30 @@ "width": null } }, - "798cb5dd7eb545d5a4188e416dce6f88": { + "78baba5e6db44fa6a19c704bf00fb60c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f841644ac2b04256b2f3c4ad9a4db82f", + "placeholder": "​", + "style": "IPY_MODEL_67269dc6c7a64c2a85d31a7de8c68e5a", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "78c7d82d53664b95a386ec96a27b6453": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2319,7 +2251,31 @@ "text_color": null } }, - "7ba99fd178de4f25867b6e37949a6d85": { + "7aac7a5b86074e509ae85dca28a96569": { + "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_99009d85bc164733af718beec07a0dd3", + "IPY_MODEL_4a0472fddab34db383019c14f53513c2", + "IPY_MODEL_467e4df618db47e2aa4db7b4eeb1b07e" + ], + "layout": "IPY_MODEL_14d064897b934c9fa649860e4a0d136f", + "tabbable": null, + "tooltip": null + } + }, + "7f6ff60608894304be071471768422d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2372,7 +2328,31 @@ "width": null } }, - "7ca38434b4e44b5b86b173c7855b4ff3": { + "86394b0091ab4767834e877eb97c7f29": { + "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_fdc61f1ca4794f988ad26aa36f51cded", + "IPY_MODEL_c26558f6f8394f7483fa04455db2ead3", + "IPY_MODEL_34e4112c8e594ad7811823c85546de7c" + ], + "layout": "IPY_MODEL_da43246f2ddc4df9968898f36b1bfc0c", + "tabbable": null, + "tooltip": null + } + }, + "89ca8bee4d29420391f60780a4e5bb37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2390,7 +2370,7 @@ "text_color": null } }, - "819ab4db9b9249cfb05199eec4ffe2ae": { + "8c24d36de7d04ba8a98dbc6243bffa1e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2443,7 +2423,31 @@ "width": null } }, - "8b0da1e1f92449e49814a4792b3a0a18": { + "9001078dd11947c3b743e880d41d89e0": { + "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_78baba5e6db44fa6a19c704bf00fb60c", + "IPY_MODEL_99c304eddb9d4f808e1444eda066b707", + "IPY_MODEL_be4a812783c5484dbde2feef6424ebe3" + ], + "layout": "IPY_MODEL_9198ec1e4cbc45a5bbf7c1f0e8976f79", + "tabbable": null, + "tooltip": null + } + }, + "9198ec1e4cbc45a5bbf7c1f0e8976f79": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2496,7 +2500,46 @@ "width": null } }, - "9a1d64682df846808b68e96652571971": { + "946fc47d18224c0287afaa056d02fa86": { + "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": "" + } + }, + "99009d85bc164733af718beec07a0dd3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_73080f2ef1784e73947c31dfebce4e08", + "placeholder": "​", + "style": "IPY_MODEL_fe5e77a095e54cad8e7f53fae0f07637", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" + } + }, + "99c304eddb9d4f808e1444eda066b707": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2512,17 +2555,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8b0da1e1f92449e49814a4792b3a0a18", - "max": 16887676.0, + "layout": "IPY_MODEL_b67e2b3c22e243868a621e764d7615bd", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_fa5d63111e8040c8a46a0ef606a7b541", + "style": "IPY_MODEL_3cf8422c429b489fa3609423681b6f05", "tabbable": null, "tooltip": null, - "value": 16887676.0 + "value": 3201.0 } }, - "9f5000cf7d6a4079b14d5f3c666d8d9a": { + "a7073edae4e4487fb17ec295efaf2195": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2575,30 +2618,7 @@ "width": null } }, - "a79c4f7046b44587aab8c79e98299302": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_701065566fa2417290be013d76599838", - "placeholder": "​", - "style": "IPY_MODEL_798cb5dd7eb545d5a4188e416dce6f88", - "tabbable": null, - "tooltip": null, - "value": "label_encoder.txt: 100%" - } - }, - "b420c63e4f174bba8f48175457a23dfe": { + "a77c2cc33dd84816aca2750dfa77985f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2616,30 +2636,31 @@ "text_color": null } }, - "b4b3cc9f4e4c408998a1c0ebec86d5bc": { + "aea522f486884287934824875f65568c": { "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_71c1b39c84cf47b5a477e26da271fef9", - "placeholder": "​", - "style": "IPY_MODEL_7ca38434b4e44b5b86b173c7855b4ff3", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e83d24db49ae4489ac3ce5c785ee2c56", + "IPY_MODEL_54fd655fb2ea4e58ba287b8a699358a4", + "IPY_MODEL_fb154ca6183045b2bcadfc5b92658f12" + ], + "layout": "IPY_MODEL_5704c0dc0c934dab903a45047e08bfa1", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" + "tooltip": null } }, - "b8e950254c7c4bfc904715885aa32fa1": { + "b67e2b3c22e243868a621e764d7615bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2692,57 +2713,7 @@ "width": null } }, - "b97eebf068ca4e9c85cbadbb4cc103c4": { - "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": "" - } - }, - "c0dd17ee1b414e0dbba6940c708a7553": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c12a918e266b40caa2ad3eb5ba27297c": { - "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": "" - } - }, - "c920632573cc4fe0976140070f9677ec": { + "b831f552387747248fdc25346996cacd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2795,7 +2766,7 @@ "width": null } }, - "c979e50a880a49aeaf6e580f1f3ff7e8": { + "be4a812783c5484dbde2feef6424ebe3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2810,15 +2781,75 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4c6822f78a694818a65c111aed9cd2c2", + "layout": "IPY_MODEL_8c24d36de7d04ba8a98dbc6243bffa1e", "placeholder": "​", - "style": "IPY_MODEL_e3c6b0d1ab4745c796d69b14cb880bf5", + "style": "IPY_MODEL_c4f6d1fdbf7f4c11b3b58973c52c1d0d", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 212MB/s]" + "value": " 3.20k/3.20k [00:00<00:00, 792kB/s]" } }, - "d563179a4e534dd4b15465aa4a240b93": { + "c26558f6f8394f7483fa04455db2ead3": { + "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_2d3c1dc1060c4165802a43d8a1506254", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_946fc47d18224c0287afaa056d02fa86", + "tabbable": null, + "tooltip": null, + "value": 2041.0 + } + }, + "c4f6d1fdbf7f4c11b3b58973c52c1d0d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "c9789735e7aa44fb8c6bcf3f8ceeebf2": { + "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": "" + } + }, + "d12f8fa702da44f6b81f1b84c1c76383": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2871,7 +2902,7 @@ "width": null } }, - "dd83345f7f5c46a38920bb555c0f6b9a": { + "da43246f2ddc4df9968898f36b1bfc0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2924,48 +2955,33 @@ "width": null } }, - "de328081cf5143bda2f18a1f732277b0": { + "db99e931b4c94d2fb4385e2ccfaa8522": { "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_10945e8601a446f2bb59fa1211f86f5b", - "placeholder": "​", - "style": "IPY_MODEL_214135184cdb4c61851d09d4776b8681", + "layout": "IPY_MODEL_73e424d16cbf4e40ad3206856f075d5b", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2e538bcb8e2a4e88a9d22def0a5b4c06", "tabbable": null, "tooltip": null, - "value": " 129k/129k [00:00<00:00, 24.4MB/s]" - } - }, - "e3c6b0d1ab4745c796d69b14cb880bf5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_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": 15856877.0 } }, - "e41b0c6637564566b921cc1987c4bf9a": { + "e7fc281836224420a165582ad5a1f00e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3018,31 +3034,7 @@ "width": null } }, - "e4e9c1fa715d49009ec1097cf561d5f6": { - "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_6a89c26dda78427781977305cd34e44b", - "IPY_MODEL_9a1d64682df846808b68e96652571971", - "IPY_MODEL_08327f8f533f49bb8518d3413af11e27" - ], - "layout": "IPY_MODEL_819ab4db9b9249cfb05199eec4ffe2ae", - "tabbable": null, - "tooltip": null - } - }, - "ef0ba41ae31d4fca866f944d42378821": { + "e83d24db49ae4489ac3ce5c785ee2c56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3057,55 +3049,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_700bb6482b2c4111b4ed9390c8470861", + "layout": "IPY_MODEL_7f6ff60608894304be071471768422d5", "placeholder": "​", - "style": "IPY_MODEL_f9b3a44be9f34f6e93de758ee46b92ec", + "style": "IPY_MODEL_89ca8bee4d29420391f60780a4e5bb37", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "f321f0e3281b4b478dffcfb404472020": { - "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": "" - } - }, - "f814d21bb5204a479acad09d629678fa": { - "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_a79c4f7046b44587aab8c79e98299302", - "IPY_MODEL_15a248e8576b4e1cace7306d79423606", - "IPY_MODEL_de328081cf5143bda2f18a1f732277b0" - ], - "layout": "IPY_MODEL_b8e950254c7c4bfc904715885aa32fa1", - "tabbable": null, - "tooltip": null + "value": "embedding_model.ckpt: 100%" } }, - "f948f7fae2bd4b23b89c1a9f86de6cdc": { + "f841644ac2b04256b2f3c4ad9a4db82f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3158,7 +3110,30 @@ "width": null } }, - "f9b3a44be9f34f6e93de758ee46b92ec": { + "fb154ca6183045b2bcadfc5b92658f12": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a7073edae4e4487fb17ec295efaf2195", + "placeholder": "​", + "style": "IPY_MODEL_62b1b0b90a684d3488af171459b2e05b", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 56.5MB/s]" + } + }, + "fb8ea71a45ce49d1bc0bf64d3de435a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3176,20 +3151,45 @@ "text_color": null } }, - "fa5d63111e8040c8a46a0ef606a7b541": { + "fdc61f1ca4794f988ad26aa36f51cded": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_419fb750a9174f9dbee80f1946cac5e2", + "placeholder": "​", + "style": "IPY_MODEL_04992c51b0da458e880d33ce9b344519", + "tabbable": null, + "tooltip": null, + "value": "hyperparams.yaml: 100%" + } + }, + "fe5e77a095e54cad8e7f53fae0f07637": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index 5f0020f03..db4995cbb 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1295,7 +1295,7 @@

Functionality 3: Save and load Datalab objects

-
+
@@ -1570,7 +1570,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"ec547a92716a409bb8eb86bc364258c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ba52f0b8569f404584f54443a28a0baf": {"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": ""}}, "4440624d361b4a39a470c6b36d42b8d3": {"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_ec547a92716a409bb8eb86bc364258c9", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ba52f0b8569f404584f54443a28a0baf", "tabbable": null, "tooltip": null, "value": 132.0}}, "55ed31e23a4a443bbb5e734bb143d697": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fd6266d59b69432181af01e5eb3c389d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "890c901184904e0883bb0bded31d86de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_55ed31e23a4a443bbb5e734bb143d697", "placeholder": "\u200b", "style": "IPY_MODEL_fd6266d59b69432181af01e5eb3c389d", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "eae4cf97733c4548920387dc447b9d98": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1b84860876254aa29989a5bb614dca8d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8e898dc9dd204b2f8ba985adc383a396": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_eae4cf97733c4548920387dc447b9d98", "placeholder": "\u200b", "style": "IPY_MODEL_1b84860876254aa29989a5bb614dca8d", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713525.06\u2007examples/s]"}}, "94329361a5b3479e804741fa80c47e78": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e8a80b5b1ace4f0e9399969281df7d06": {"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_890c901184904e0883bb0bded31d86de", "IPY_MODEL_4440624d361b4a39a470c6b36d42b8d3", "IPY_MODEL_8e898dc9dd204b2f8ba985adc383a396"], "layout": "IPY_MODEL_94329361a5b3479e804741fa80c47e78", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"ed4df9f1de274611a1946fa0e269d33e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e56307db7d2e450c9f2c4b97981eee9d": {"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": ""}}, "ec22ebaf4e91499784ef8ac8e966a147": {"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_ed4df9f1de274611a1946fa0e269d33e", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e56307db7d2e450c9f2c4b97981eee9d", "tabbable": null, "tooltip": null, "value": 132.0}}, "37952b8d6afc47f2b9ed08ee7e66d264": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "da7692e003c04b5f8f1de02ea7e688c1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b144d01abdc746e495df91b32d9d09e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_37952b8d6afc47f2b9ed08ee7e66d264", "placeholder": "\u200b", "style": "IPY_MODEL_da7692e003c04b5f8f1de02ea7e688c1", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "c51595d098f645f0a7967c34f586fa27": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "48eeb0e9ec1b440e9b49518e8338af15": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f4567689e9ee49d28457ae1b5cdc262a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c51595d098f645f0a7967c34f586fa27", "placeholder": "\u200b", "style": "IPY_MODEL_48eeb0e9ec1b440e9b49518e8338af15", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200711171.72\u2007examples/s]"}}, "9602309f72214bd0b17e007cb02789b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "84640571afb64f84bab623cedce4b8ca": {"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_b144d01abdc746e495df91b32d9d09e8", "IPY_MODEL_ec22ebaf4e91499784ef8ac8e966a147", "IPY_MODEL_f4567689e9ee49d28457ae1b5cdc262a"], "layout": "IPY_MODEL_9602309f72214bd0b17e007cb02789b7", "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 1028deca4..f581760ef 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": "2024-09-06T19:33:42.774016Z", - "iopub.status.busy": "2024-09-06T19:33:42.773836Z", - "iopub.status.idle": "2024-09-06T19:33:43.987649Z", - "shell.execute_reply": "2024-09-06T19:33:43.987087Z" + "iopub.execute_input": "2024-09-26T14:47:45.611697Z", + "iopub.status.busy": "2024-09-26T14:47:45.611515Z", + "iopub.status.idle": "2024-09-26T14:47:46.872000Z", + "shell.execute_reply": "2024-09-26T14:47:46.871368Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:43.990323Z", - "iopub.status.busy": "2024-09-06T19:33:43.989863Z", - "iopub.status.idle": "2024-09-06T19:33:43.992901Z", - "shell.execute_reply": "2024-09-06T19:33:43.992377Z" + "iopub.execute_input": "2024-09-26T14:47:46.874219Z", + "iopub.status.busy": "2024-09-26T14:47:46.873943Z", + "iopub.status.idle": "2024-09-26T14:47:46.877182Z", + "shell.execute_reply": "2024-09-26T14:47:46.876630Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:43.995557Z", - "iopub.status.busy": "2024-09-06T19:33:43.995114Z", - "iopub.status.idle": "2024-09-06T19:33:44.005313Z", - "shell.execute_reply": "2024-09-06T19:33:44.004712Z" + "iopub.execute_input": "2024-09-26T14:47:46.878965Z", + "iopub.status.busy": "2024-09-26T14:47:46.878784Z", + "iopub.status.idle": "2024-09-26T14:47:46.887523Z", + "shell.execute_reply": "2024-09-26T14:47:46.887072Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.007445Z", - "iopub.status.busy": "2024-09-06T19:33:44.007143Z", - "iopub.status.idle": "2024-09-06T19:33:44.012113Z", - "shell.execute_reply": "2024-09-06T19:33:44.011528Z" + "iopub.execute_input": "2024-09-26T14:47:46.889337Z", + "iopub.status.busy": "2024-09-26T14:47:46.889146Z", + "iopub.status.idle": "2024-09-26T14:47:46.893734Z", + "shell.execute_reply": "2024-09-26T14:47:46.893242Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.014273Z", - "iopub.status.busy": "2024-09-06T19:33:44.013973Z", - "iopub.status.idle": "2024-09-06T19:33:44.198978Z", - "shell.execute_reply": "2024-09-06T19:33:44.198432Z" + "iopub.execute_input": "2024-09-26T14:47:46.895681Z", + "iopub.status.busy": "2024-09-26T14:47:46.895273Z", + "iopub.status.idle": "2024-09-26T14:47:47.085029Z", + "shell.execute_reply": "2024-09-26T14:47:47.084376Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.201674Z", - "iopub.status.busy": "2024-09-06T19:33:44.201189Z", - "iopub.status.idle": "2024-09-06T19:33:44.572697Z", - "shell.execute_reply": "2024-09-06T19:33:44.572078Z" + "iopub.execute_input": "2024-09-26T14:47:47.087608Z", + "iopub.status.busy": "2024-09-26T14:47:47.087115Z", + "iopub.status.idle": "2024-09-26T14:47:47.421574Z", + "shell.execute_reply": "2024-09-26T14:47:47.420964Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.574957Z", - "iopub.status.busy": "2024-09-06T19:33:44.574618Z", - "iopub.status.idle": "2024-09-06T19:33:44.598417Z", - "shell.execute_reply": "2024-09-06T19:33:44.597853Z" + "iopub.execute_input": "2024-09-26T14:47:47.423413Z", + "iopub.status.busy": "2024-09-26T14:47:47.423220Z", + "iopub.status.idle": "2024-09-26T14:47:47.447494Z", + "shell.execute_reply": "2024-09-26T14:47:47.447007Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.600565Z", - "iopub.status.busy": "2024-09-06T19:33:44.600242Z", - "iopub.status.idle": "2024-09-06T19:33:44.611542Z", - "shell.execute_reply": "2024-09-06T19:33:44.611124Z" + "iopub.execute_input": "2024-09-26T14:47:47.449677Z", + "iopub.status.busy": "2024-09-26T14:47:47.449174Z", + "iopub.status.idle": "2024-09-26T14:47:47.542304Z", + "shell.execute_reply": "2024-09-26T14:47:47.541668Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:44.613571Z", - "iopub.status.busy": "2024-09-06T19:33:44.613277Z", - "iopub.status.idle": "2024-09-06T19:33:46.668917Z", - "shell.execute_reply": "2024-09-06T19:33:46.668293Z" + "iopub.execute_input": "2024-09-26T14:47:47.544443Z", + "iopub.status.busy": "2024-09-26T14:47:47.544249Z", + "iopub.status.idle": "2024-09-26T14:47:49.580917Z", + "shell.execute_reply": "2024-09-26T14:47:49.580350Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.671429Z", - "iopub.status.busy": "2024-09-06T19:33:46.670969Z", - "iopub.status.idle": "2024-09-06T19:33:46.692283Z", - "shell.execute_reply": "2024-09-06T19:33:46.691706Z" + "iopub.execute_input": "2024-09-26T14:47:49.583198Z", + "iopub.status.busy": "2024-09-26T14:47:49.582700Z", + "iopub.status.idle": "2024-09-26T14:47:49.606247Z", + "shell.execute_reply": "2024-09-26T14:47:49.605757Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.694643Z", - "iopub.status.busy": "2024-09-06T19:33:46.694120Z", - "iopub.status.idle": "2024-09-06T19:33:46.711969Z", - "shell.execute_reply": "2024-09-06T19:33:46.711526Z" + "iopub.execute_input": "2024-09-26T14:47:49.608105Z", + "iopub.status.busy": "2024-09-26T14:47:49.607920Z", + "iopub.status.idle": "2024-09-26T14:47:49.626258Z", + "shell.execute_reply": "2024-09-26T14:47:49.625780Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.713865Z", - "iopub.status.busy": "2024-09-06T19:33:46.713694Z", - "iopub.status.idle": "2024-09-06T19:33:46.728067Z", - "shell.execute_reply": "2024-09-06T19:33:46.727609Z" + "iopub.execute_input": "2024-09-26T14:47:49.628144Z", + "iopub.status.busy": "2024-09-26T14:47:49.627801Z", + "iopub.status.idle": "2024-09-26T14:47:49.641555Z", + "shell.execute_reply": "2024-09-26T14:47:49.641075Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.729970Z", - "iopub.status.busy": "2024-09-06T19:33:46.729797Z", - "iopub.status.idle": "2024-09-06T19:33:46.748313Z", - "shell.execute_reply": "2024-09-06T19:33:46.747746Z" + "iopub.execute_input": "2024-09-26T14:47:49.643368Z", + "iopub.status.busy": "2024-09-26T14:47:49.643024Z", + "iopub.status.idle": "2024-09-26T14:47:49.664797Z", + "shell.execute_reply": "2024-09-26T14:47:49.664316Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8a80b5b1ace4f0e9399969281df7d06", + "model_id": "84640571afb64f84bab623cedce4b8ca", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.750540Z", - "iopub.status.busy": "2024-09-06T19:33:46.750202Z", - "iopub.status.idle": "2024-09-06T19:33:46.765277Z", - "shell.execute_reply": "2024-09-06T19:33:46.764810Z" + "iopub.execute_input": "2024-09-26T14:47:49.666763Z", + "iopub.status.busy": "2024-09-26T14:47:49.666313Z", + "iopub.status.idle": "2024-09-26T14:47:49.681745Z", + "shell.execute_reply": "2024-09-26T14:47:49.681144Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.767376Z", - "iopub.status.busy": "2024-09-06T19:33:46.767048Z", - "iopub.status.idle": "2024-09-06T19:33:46.772946Z", - "shell.execute_reply": "2024-09-06T19:33:46.772447Z" + "iopub.execute_input": "2024-09-26T14:47:49.683800Z", + "iopub.status.busy": "2024-09-26T14:47:49.683338Z", + "iopub.status.idle": "2024-09-26T14:47:49.689263Z", + "shell.execute_reply": "2024-09-26T14:47:49.688776Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:46.774871Z", - "iopub.status.busy": "2024-09-06T19:33:46.774606Z", - "iopub.status.idle": "2024-09-06T19:33:46.792743Z", - "shell.execute_reply": "2024-09-06T19:33:46.792272Z" + "iopub.execute_input": "2024-09-26T14:47:49.691065Z", + "iopub.status.busy": "2024-09-26T14:47:49.690751Z", + "iopub.status.idle": "2024-09-26T14:47:49.709104Z", + "shell.execute_reply": "2024-09-26T14:47:49.708511Z" } }, "outputs": [ @@ -1437,7 +1437,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { @@ -1447,51 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1b84860876254aa29989a5bb614dca8d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4440624d361b4a39a470c6b36d42b8d3": { - "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_ec547a92716a409bb8eb86bc364258c9", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ba52f0b8569f404584f54443a28a0baf", - "tabbable": null, - "tooltip": null, - "value": 132.0 - } - }, - "55ed31e23a4a443bbb5e734bb143d697": { + "37952b8d6afc47f2b9ed08ee7e66d264": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1544,53 +1500,49 @@ "width": null } }, - "890c901184904e0883bb0bded31d86de": { + "48eeb0e9ec1b440e9b49518e8338af15": { "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_55ed31e23a4a443bbb5e734bb143d697", - "placeholder": "​", - "style": "IPY_MODEL_fd6266d59b69432181af01e5eb3c389d", - "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 } }, - "8e898dc9dd204b2f8ba985adc383a396": { + "84640571afb64f84bab623cedce4b8ca": { "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_eae4cf97733c4548920387dc447b9d98", - "placeholder": "​", - "style": "IPY_MODEL_1b84860876254aa29989a5bb614dca8d", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b144d01abdc746e495df91b32d9d09e8", + "IPY_MODEL_ec22ebaf4e91499784ef8ac8e966a147", + "IPY_MODEL_f4567689e9ee49d28457ae1b5cdc262a" + ], + "layout": "IPY_MODEL_9602309f72214bd0b17e007cb02789b7", "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 13525.06 examples/s]" + "tooltip": null } }, - "94329361a5b3479e804741fa80c47e78": { + "9602309f72214bd0b17e007cb02789b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1643,47 +1595,30 @@ "width": null } }, - "ba52f0b8569f404584f54443a28a0baf": { - "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": "" - } - }, - "e8a80b5b1ace4f0e9399969281df7d06": { + "b144d01abdc746e495df91b32d9d09e8": { "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_890c901184904e0883bb0bded31d86de", - "IPY_MODEL_4440624d361b4a39a470c6b36d42b8d3", - "IPY_MODEL_8e898dc9dd204b2f8ba985adc383a396" - ], - "layout": "IPY_MODEL_94329361a5b3479e804741fa80c47e78", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_37952b8d6afc47f2b9ed08ee7e66d264", + "placeholder": "​", + "style": "IPY_MODEL_da7692e003c04b5f8f1de02ea7e688c1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Saving the dataset (1/1 shards): 100%" } }, - "eae4cf97733c4548920387dc447b9d98": { + "c51595d098f645f0a7967c34f586fa27": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1736,7 +1671,67 @@ "width": null } }, - "ec547a92716a409bb8eb86bc364258c9": { + "da7692e003c04b5f8f1de02ea7e688c1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "e56307db7d2e450c9f2c4b97981eee9d": { + "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": "" + } + }, + "ec22ebaf4e91499784ef8ac8e966a147": { + "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_ed4df9f1de274611a1946fa0e269d33e", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e56307db7d2e450c9f2c4b97981eee9d", + "tabbable": null, + "tooltip": null, + "value": 132.0 + } + }, + "ed4df9f1de274611a1946fa0e269d33e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1789,22 +1784,27 @@ "width": null } }, - "fd6266d59b69432181af01e5eb3c389d": { + "f4567689e9ee49d28457ae1b5cdc262a": { "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_c51595d098f645f0a7967c34f586fa27", + "placeholder": "​", + "style": "IPY_MODEL_48eeb0e9ec1b440e9b49518e8338af15", + "tabbable": null, + "tooltip": null, + "value": " 132/132 [00:00<00:00, 11171.72 examples/s]" } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index a75ce5d83..767bffe7d 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": "2024-09-06T19:33:49.692668Z", - "iopub.status.busy": "2024-09-06T19:33:49.692505Z", - "iopub.status.idle": "2024-09-06T19:33:50.890931Z", - "shell.execute_reply": "2024-09-06T19:33:50.890368Z" + "iopub.execute_input": "2024-09-26T14:47:52.531871Z", + "iopub.status.busy": "2024-09-26T14:47:52.531690Z", + "iopub.status.idle": "2024-09-26T14:47:53.801759Z", + "shell.execute_reply": "2024-09-26T14:47:53.801164Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:33:50.893481Z", - "iopub.status.busy": "2024-09-06T19:33:50.892976Z", - "iopub.status.idle": "2024-09-06T19:33:50.895994Z", - "shell.execute_reply": "2024-09-06T19:33:50.895546Z" + "iopub.execute_input": "2024-09-26T14:47:53.804080Z", + "iopub.status.busy": "2024-09-26T14:47:53.803489Z", + "iopub.status.idle": "2024-09-26T14:47:53.806671Z", + "shell.execute_reply": "2024-09-26T14:47:53.806214Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.898095Z", - "iopub.status.busy": "2024-09-06T19:33:50.897919Z", - "iopub.status.idle": "2024-09-06T19:33:50.907050Z", - "shell.execute_reply": "2024-09-06T19:33:50.906577Z" + "iopub.execute_input": "2024-09-26T14:47:53.808602Z", + "iopub.status.busy": "2024-09-26T14:47:53.808274Z", + "iopub.status.idle": "2024-09-26T14:47:53.817439Z", + "shell.execute_reply": "2024-09-26T14:47:53.816846Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.908860Z", - "iopub.status.busy": "2024-09-06T19:33:50.908672Z", - "iopub.status.idle": "2024-09-06T19:33:50.913284Z", - "shell.execute_reply": "2024-09-06T19:33:50.912693Z" + "iopub.execute_input": "2024-09-26T14:47:53.819218Z", + "iopub.status.busy": "2024-09-26T14:47:53.818813Z", + "iopub.status.idle": "2024-09-26T14:47:53.823869Z", + "shell.execute_reply": "2024-09-26T14:47:53.823416Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:50.915417Z", - "iopub.status.busy": "2024-09-06T19:33:50.915238Z", - "iopub.status.idle": "2024-09-06T19:33:51.099306Z", - "shell.execute_reply": "2024-09-06T19:33:51.098789Z" + "iopub.execute_input": "2024-09-26T14:47:53.825624Z", + "iopub.status.busy": "2024-09-26T14:47:53.825446Z", + "iopub.status.idle": "2024-09-26T14:47:54.012981Z", + "shell.execute_reply": "2024-09-26T14:47:54.012359Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.101790Z", - "iopub.status.busy": "2024-09-06T19:33:51.101450Z", - "iopub.status.idle": "2024-09-06T19:33:51.473593Z", - "shell.execute_reply": "2024-09-06T19:33:51.473003Z" + "iopub.execute_input": "2024-09-26T14:47:54.015114Z", + "iopub.status.busy": "2024-09-26T14:47:54.014923Z", + "iopub.status.idle": "2024-09-26T14:47:54.396149Z", + "shell.execute_reply": "2024-09-26T14:47:54.395584Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.475866Z", - "iopub.status.busy": "2024-09-06T19:33:51.475414Z", - "iopub.status.idle": "2024-09-06T19:33:51.478399Z", - "shell.execute_reply": "2024-09-06T19:33:51.477816Z" + "iopub.execute_input": "2024-09-26T14:47:54.398064Z", + "iopub.status.busy": "2024-09-26T14:47:54.397698Z", + "iopub.status.idle": "2024-09-26T14:47:54.400577Z", + "shell.execute_reply": "2024-09-26T14:47:54.400116Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.480745Z", - "iopub.status.busy": "2024-09-06T19:33:51.480341Z", - "iopub.status.idle": "2024-09-06T19:33:51.514306Z", - "shell.execute_reply": "2024-09-06T19:33:51.513859Z" + "iopub.execute_input": "2024-09-26T14:47:54.402366Z", + "iopub.status.busy": "2024-09-26T14:47:54.402019Z", + "iopub.status.idle": "2024-09-26T14:47:54.437650Z", + "shell.execute_reply": "2024-09-26T14:47:54.437009Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:51.516441Z", - "iopub.status.busy": "2024-09-06T19:33:51.516020Z", - "iopub.status.idle": "2024-09-06T19:33:53.590850Z", - "shell.execute_reply": "2024-09-06T19:33:53.590263Z" + "iopub.execute_input": "2024-09-26T14:47:54.439903Z", + "iopub.status.busy": "2024-09-26T14:47:54.439564Z", + "iopub.status.idle": "2024-09-26T14:47:56.611360Z", + "shell.execute_reply": "2024-09-26T14:47:56.610777Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.593403Z", - "iopub.status.busy": "2024-09-06T19:33:53.592894Z", - "iopub.status.idle": "2024-09-06T19:33:53.611543Z", - "shell.execute_reply": "2024-09-06T19:33:53.610984Z" + "iopub.execute_input": "2024-09-26T14:47:56.613533Z", + "iopub.status.busy": "2024-09-26T14:47:56.612962Z", + "iopub.status.idle": "2024-09-26T14:47:56.632035Z", + "shell.execute_reply": "2024-09-26T14:47:56.631583Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.613666Z", - "iopub.status.busy": "2024-09-06T19:33:53.613354Z", - "iopub.status.idle": "2024-09-06T19:33:53.619845Z", - "shell.execute_reply": "2024-09-06T19:33:53.619296Z" + "iopub.execute_input": "2024-09-26T14:47:56.633836Z", + "iopub.status.busy": "2024-09-26T14:47:56.633486Z", + "iopub.status.idle": "2024-09-26T14:47:56.639903Z", + "shell.execute_reply": "2024-09-26T14:47:56.639463Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.621866Z", - "iopub.status.busy": "2024-09-06T19:33:53.621559Z", - "iopub.status.idle": "2024-09-06T19:33:53.628504Z", - "shell.execute_reply": "2024-09-06T19:33:53.627959Z" + "iopub.execute_input": "2024-09-26T14:47:56.641707Z", + "iopub.status.busy": "2024-09-26T14:47:56.641368Z", + "iopub.status.idle": "2024-09-26T14:47:56.647367Z", + "shell.execute_reply": "2024-09-26T14:47:56.646803Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.630721Z", - "iopub.status.busy": "2024-09-06T19:33:53.630404Z", - "iopub.status.idle": "2024-09-06T19:33:53.640976Z", - "shell.execute_reply": "2024-09-06T19:33:53.640522Z" + "iopub.execute_input": "2024-09-26T14:47:56.649030Z", + "iopub.status.busy": "2024-09-26T14:47:56.648855Z", + "iopub.status.idle": "2024-09-26T14:47:56.659402Z", + "shell.execute_reply": "2024-09-26T14:47:56.658957Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.643037Z", - "iopub.status.busy": "2024-09-06T19:33:53.642719Z", - "iopub.status.idle": "2024-09-06T19:33:53.651678Z", - "shell.execute_reply": "2024-09-06T19:33:53.651115Z" + "iopub.execute_input": "2024-09-26T14:47:56.661038Z", + "iopub.status.busy": "2024-09-26T14:47:56.660772Z", + "iopub.status.idle": "2024-09-26T14:47:56.669970Z", + "shell.execute_reply": "2024-09-26T14:47:56.669408Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.653852Z", - "iopub.status.busy": "2024-09-06T19:33:53.653447Z", - "iopub.status.idle": "2024-09-06T19:33:53.660374Z", - "shell.execute_reply": "2024-09-06T19:33:53.659816Z" + "iopub.execute_input": "2024-09-26T14:47:56.671720Z", + "iopub.status.busy": "2024-09-26T14:47:56.671331Z", + "iopub.status.idle": "2024-09-26T14:47:56.678074Z", + "shell.execute_reply": "2024-09-26T14:47:56.677629Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.662428Z", - "iopub.status.busy": "2024-09-06T19:33:53.662108Z", - "iopub.status.idle": "2024-09-06T19:33:53.671181Z", - "shell.execute_reply": "2024-09-06T19:33:53.670717Z" + "iopub.execute_input": "2024-09-26T14:47:56.679875Z", + "iopub.status.busy": "2024-09-26T14:47:56.679423Z", + "iopub.status.idle": "2024-09-26T14:47:56.689042Z", + "shell.execute_reply": "2024-09-26T14:47:56.688474Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:53.673080Z", - "iopub.status.busy": "2024-09-06T19:33:53.672905Z", - "iopub.status.idle": "2024-09-06T19:33:53.689334Z", - "shell.execute_reply": "2024-09-06T19:33:53.688736Z" + "iopub.execute_input": "2024-09-26T14:47:56.690832Z", + "iopub.status.busy": "2024-09-26T14:47:56.690435Z", + "iopub.status.idle": "2024-09-26T14:47:56.707572Z", + "shell.execute_reply": "2024-09-26T14:47:56.706989Z" }, "nbsphinx": "hidden" }, @@ -1648,7 +1648,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 116c9e30a..832682f21 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -731,31 +731,31 @@

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.

@@ -1068,7 +1068,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1100,7 +1100,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -1132,7 +1132,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
+
@@ -2046,35 +2046,35 @@

Low information images - low_information_score is_low_information_issue + low_information_score 53050 - 0.067975 True + 0.067975 40875 - 0.089929 True + 0.089929 9594 - 0.092601 True + 0.092601 34825 - 0.107744 True + 0.107744 37530 - 0.108516 True + 0.108516 @@ -2102,7 +2102,7 @@

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 d0e19982d..81904fb5e 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": "2024-09-06T19:33:56.342254Z", - "iopub.status.busy": "2024-09-06T19:33:56.341754Z", - "iopub.status.idle": "2024-09-06T19:33:59.356819Z", - "shell.execute_reply": "2024-09-06T19:33:59.356183Z" + "iopub.execute_input": "2024-09-26T14:47:59.485196Z", + "iopub.status.busy": "2024-09-26T14:47:59.485011Z", + "iopub.status.idle": "2024-09-26T14:48:02.622875Z", + "shell.execute_reply": "2024-09-26T14:48:02.622306Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:59.359528Z", - "iopub.status.busy": "2024-09-06T19:33:59.359236Z", - "iopub.status.idle": "2024-09-06T19:33:59.363077Z", - "shell.execute_reply": "2024-09-06T19:33:59.362504Z" + "iopub.execute_input": "2024-09-26T14:48:02.625172Z", + "iopub.status.busy": "2024-09-26T14:48:02.624685Z", + "iopub.status.idle": "2024-09-26T14:48:02.628360Z", + "shell.execute_reply": "2024-09-26T14:48:02.627898Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:33:59.365205Z", - "iopub.status.busy": "2024-09-06T19:33:59.364886Z", - "iopub.status.idle": "2024-09-06T19:34:04.314293Z", - "shell.execute_reply": "2024-09-06T19:34:04.313807Z" + "iopub.execute_input": "2024-09-26T14:48:02.630127Z", + "iopub.status.busy": "2024-09-26T14:48:02.629815Z", + "iopub.status.idle": "2024-09-26T14:48:05.745420Z", + "shell.execute_reply": "2024-09-26T14:48:05.744938Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bcf07287e5846bcade12829a0129e5a", + "model_id": "517b83c613bb49c9ab0cd319caf77fa4", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3273abc0b1474d17ad8e620a0b9cd685", + "model_id": "157b0b5de92c4dd39861100e0048c0b7", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "468f054b84de4a46abae17b5d6030a66", + "model_id": "dc9f46273db144d982f466b186d6ea8d", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85a6da0e361d4bb78dac486525795dad", + "model_id": "77e4c9ec41b8452c8963af0b77b5555f", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16923bdba0af47908931030b52eaedca", + "model_id": "cd9cd1e986f74424b60db3510b43826d", "version_major": 2, "version_minor": 0 }, @@ -260,10 +260,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:04.316479Z", - "iopub.status.busy": "2024-09-06T19:34:04.316130Z", - "iopub.status.idle": "2024-09-06T19:34:04.319984Z", - "shell.execute_reply": "2024-09-06T19:34:04.319538Z" + "iopub.execute_input": "2024-09-26T14:48:05.747100Z", + "iopub.status.busy": "2024-09-26T14:48:05.746916Z", + "iopub.status.idle": "2024-09-26T14:48:05.750921Z", + "shell.execute_reply": "2024-09-26T14:48:05.750358Z" } }, "outputs": [ @@ -288,17 +288,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:04.321997Z", - "iopub.status.busy": "2024-09-06T19:34:04.321665Z", - "iopub.status.idle": "2024-09-06T19:34:15.824023Z", - "shell.execute_reply": "2024-09-06T19:34:15.823467Z" + "iopub.execute_input": "2024-09-26T14:48:05.752502Z", + "iopub.status.busy": "2024-09-26T14:48:05.752202Z", + "iopub.status.idle": "2024-09-26T14:48:17.148896Z", + "shell.execute_reply": "2024-09-26T14:48:17.148237Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bfcb4b6339d14370bc404a61e757edfd", + "model_id": "5e114e4103d94679910f2192af574f94", "version_major": 2, "version_minor": 0 }, @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:15.826734Z", - "iopub.status.busy": "2024-09-06T19:34:15.826342Z", - "iopub.status.idle": "2024-09-06T19:34:34.591212Z", - "shell.execute_reply": "2024-09-06T19:34:34.590672Z" + "iopub.execute_input": "2024-09-26T14:48:17.151191Z", + "iopub.status.busy": "2024-09-26T14:48:17.150948Z", + "iopub.status.idle": "2024-09-26T14:48:35.261693Z", + "shell.execute_reply": "2024-09-26T14:48:35.261069Z" } }, "outputs": [], @@ -372,10 +372,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.593912Z", - "iopub.status.busy": "2024-09-06T19:34:34.593533Z", - "iopub.status.idle": "2024-09-06T19:34:34.599439Z", - "shell.execute_reply": "2024-09-06T19:34:34.598956Z" + "iopub.execute_input": "2024-09-26T14:48:35.264037Z", + "iopub.status.busy": "2024-09-26T14:48:35.263651Z", + "iopub.status.idle": "2024-09-26T14:48:35.269633Z", + "shell.execute_reply": "2024-09-26T14:48:35.269145Z" } }, "outputs": [], @@ -413,10 +413,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.601473Z", - "iopub.status.busy": "2024-09-06T19:34:34.601136Z", - "iopub.status.idle": "2024-09-06T19:34:34.604946Z", - "shell.execute_reply": "2024-09-06T19:34:34.604479Z" + "iopub.execute_input": "2024-09-26T14:48:35.271336Z", + "iopub.status.busy": "2024-09-26T14:48:35.270995Z", + "iopub.status.idle": "2024-09-26T14:48:35.274864Z", + "shell.execute_reply": "2024-09-26T14:48:35.274455Z" }, "nbsphinx": "hidden" }, @@ -553,10 +553,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.607009Z", - "iopub.status.busy": "2024-09-06T19:34:34.606678Z", - "iopub.status.idle": "2024-09-06T19:34:34.615441Z", - "shell.execute_reply": "2024-09-06T19:34:34.614962Z" + "iopub.execute_input": "2024-09-26T14:48:35.276663Z", + "iopub.status.busy": "2024-09-26T14:48:35.276341Z", + "iopub.status.idle": "2024-09-26T14:48:35.285167Z", + "shell.execute_reply": "2024-09-26T14:48:35.284719Z" }, "nbsphinx": "hidden" }, @@ -681,10 +681,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.617637Z", - "iopub.status.busy": "2024-09-06T19:34:34.617189Z", - "iopub.status.idle": "2024-09-06T19:34:34.644027Z", - "shell.execute_reply": "2024-09-06T19:34:34.643475Z" + "iopub.execute_input": "2024-09-26T14:48:35.286904Z", + "iopub.status.busy": "2024-09-26T14:48:35.286584Z", + "iopub.status.idle": "2024-09-26T14:48:35.314453Z", + "shell.execute_reply": "2024-09-26T14:48:35.313973Z" } }, "outputs": [], @@ -721,10 +721,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:34:34.646190Z", - "iopub.status.busy": "2024-09-06T19:34:34.645869Z", - "iopub.status.idle": "2024-09-06T19:35:07.856682Z", - "shell.execute_reply": "2024-09-06T19:35:07.856077Z" + "iopub.execute_input": "2024-09-26T14:48:35.316272Z", + "iopub.status.busy": "2024-09-26T14:48:35.315932Z", + "iopub.status.idle": "2024-09-26T14:49:09.563750Z", + "shell.execute_reply": "2024-09-26T14:49:09.563113Z" } }, "outputs": [ @@ -740,21 +740,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.923\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.965\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.597\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.763\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60b6605a27b343f3a046b38e2ee92eb3", + "model_id": "8c99cd03c2204dd69d220e1911ef407b", "version_major": 2, "version_minor": 0 }, @@ -775,7 +775,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "328179309f4646028e9f8909eefb6c74", + "model_id": "ccc0d279330845b8b34f60a57e76743f", "version_major": 2, "version_minor": 0 }, @@ -798,21 +798,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.922\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 5.062\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.912\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.901\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "958c94ac86804e8fbd31685a6f87d389", + "model_id": "c01e10af9cd04c4c90430d0afbaa6da0", "version_major": 2, "version_minor": 0 }, @@ -833,7 +833,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "593399f7ed16479cabf5d6887e2046b5", + "model_id": "60160531292f49f6912a5e7fa5c1cd4a", "version_major": 2, "version_minor": 0 }, @@ -856,21 +856,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.879\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 5.009\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.556\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.800\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a3d18201bb14d5c9e73af43adbe2cd8", + "model_id": "e20ec2fb456e4bb9bfb446110e53d341", "version_major": 2, "version_minor": 0 }, @@ -891,7 +891,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cef86182d7ef449481f59dfea70aa34a", + "model_id": "9c97151f1d7f4a49a3e2278cddb3c604", "version_major": 2, "version_minor": 0 }, @@ -970,10 +970,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:07.859270Z", - "iopub.status.busy": "2024-09-06T19:35:07.859022Z", - "iopub.status.idle": "2024-09-06T19:35:07.875302Z", - "shell.execute_reply": "2024-09-06T19:35:07.874880Z" + "iopub.execute_input": "2024-09-26T14:49:09.565771Z", + "iopub.status.busy": "2024-09-26T14:49:09.565547Z", + "iopub.status.idle": "2024-09-26T14:49:09.582928Z", + "shell.execute_reply": "2024-09-26T14:49:09.582459Z" } }, "outputs": [], @@ -998,10 +998,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:07.877195Z", - "iopub.status.busy": "2024-09-06T19:35:07.877017Z", - "iopub.status.idle": "2024-09-06T19:35:08.338418Z", - "shell.execute_reply": "2024-09-06T19:35:08.337844Z" + "iopub.execute_input": "2024-09-26T14:49:09.584801Z", + "iopub.status.busy": "2024-09-26T14:49:09.584617Z", + "iopub.status.idle": "2024-09-26T14:49:10.073024Z", + "shell.execute_reply": "2024-09-26T14:49:10.072472Z" } }, "outputs": [], @@ -1021,10 +1021,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:35:08.340738Z", - "iopub.status.busy": "2024-09-06T19:35:08.340554Z", - "iopub.status.idle": "2024-09-06T19:36:59.451053Z", - "shell.execute_reply": "2024-09-06T19:36:59.450444Z" + "iopub.execute_input": "2024-09-26T14:49:10.074957Z", + "iopub.status.busy": "2024-09-26T14:49:10.074773Z", + "iopub.status.idle": "2024-09-26T14:51:03.811106Z", + "shell.execute_reply": "2024-09-26T14:51:03.810393Z" } }, "outputs": [ @@ -1063,7 +1063,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8c0903ec57a4db09eef7c66d76ad798", + "model_id": "48ba164541954d109266e55290018b2b", "version_major": 2, "version_minor": 0 }, @@ -1109,10 +1109,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.453745Z", - "iopub.status.busy": "2024-09-06T19:36:59.453098Z", - "iopub.status.idle": "2024-09-06T19:36:59.910431Z", - "shell.execute_reply": "2024-09-06T19:36:59.909867Z" + "iopub.execute_input": "2024-09-26T14:51:03.813379Z", + "iopub.status.busy": "2024-09-26T14:51:03.812973Z", + "iopub.status.idle": "2024-09-26T14:51:04.287434Z", + "shell.execute_reply": "2024-09-26T14:51:04.286542Z" } }, "outputs": [ @@ -1258,10 +1258,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.913045Z", - "iopub.status.busy": "2024-09-06T19:36:59.912484Z", - "iopub.status.idle": "2024-09-06T19:36:59.974160Z", - "shell.execute_reply": "2024-09-06T19:36:59.973682Z" + "iopub.execute_input": "2024-09-26T14:51:04.289772Z", + "iopub.status.busy": "2024-09-26T14:51:04.289550Z", + "iopub.status.idle": "2024-09-26T14:51:04.352405Z", + "shell.execute_reply": "2024-09-26T14:51:04.351866Z" } }, "outputs": [ @@ -1365,10 +1365,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.976368Z", - "iopub.status.busy": "2024-09-06T19:36:59.976019Z", - "iopub.status.idle": "2024-09-06T19:36:59.984805Z", - "shell.execute_reply": "2024-09-06T19:36:59.984360Z" + "iopub.execute_input": "2024-09-26T14:51:04.354105Z", + "iopub.status.busy": "2024-09-26T14:51:04.353929Z", + "iopub.status.idle": "2024-09-26T14:51:04.362590Z", + "shell.execute_reply": "2024-09-26T14:51:04.362148Z" } }, "outputs": [ @@ -1498,10 +1498,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.986926Z", - "iopub.status.busy": "2024-09-06T19:36:59.986597Z", - "iopub.status.idle": "2024-09-06T19:36:59.991039Z", - "shell.execute_reply": "2024-09-06T19:36:59.990559Z" + "iopub.execute_input": "2024-09-26T14:51:04.364304Z", + "iopub.status.busy": "2024-09-26T14:51:04.364127Z", + "iopub.status.idle": "2024-09-26T14:51:04.368705Z", + "shell.execute_reply": "2024-09-26T14:51:04.368265Z" }, "nbsphinx": "hidden" }, @@ -1547,10 +1547,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:36:59.992932Z", - "iopub.status.busy": "2024-09-06T19:36:59.992715Z", - "iopub.status.idle": "2024-09-06T19:37:00.505081Z", - "shell.execute_reply": "2024-09-06T19:37:00.504451Z" + "iopub.execute_input": "2024-09-26T14:51:04.370241Z", + "iopub.status.busy": "2024-09-26T14:51:04.370067Z", + "iopub.status.idle": "2024-09-26T14:51:04.889724Z", + "shell.execute_reply": "2024-09-26T14:51:04.889048Z" } }, "outputs": [ @@ -1585,10 +1585,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.507663Z", - "iopub.status.busy": "2024-09-06T19:37:00.507293Z", - "iopub.status.idle": "2024-09-06T19:37:00.516488Z", - "shell.execute_reply": "2024-09-06T19:37:00.515888Z" + "iopub.execute_input": "2024-09-26T14:51:04.892026Z", + "iopub.status.busy": "2024-09-26T14:51:04.891753Z", + "iopub.status.idle": "2024-09-26T14:51:04.901156Z", + "shell.execute_reply": "2024-09-26T14:51:04.900641Z" } }, "outputs": [ @@ -1755,10 +1755,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.518970Z", - "iopub.status.busy": "2024-09-06T19:37:00.518520Z", - "iopub.status.idle": "2024-09-06T19:37:00.525985Z", - "shell.execute_reply": "2024-09-06T19:37:00.525525Z" + "iopub.execute_input": "2024-09-26T14:51:04.903329Z", + "iopub.status.busy": "2024-09-26T14:51:04.902914Z", + "iopub.status.idle": "2024-09-26T14:51:04.911571Z", + "shell.execute_reply": "2024-09-26T14:51:04.910975Z" }, "nbsphinx": "hidden" }, @@ -1834,10 +1834,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.528061Z", - "iopub.status.busy": "2024-09-06T19:37:00.527749Z", - "iopub.status.idle": "2024-09-06T19:37:00.996315Z", - "shell.execute_reply": "2024-09-06T19:37:00.995664Z" + "iopub.execute_input": "2024-09-26T14:51:04.913701Z", + "iopub.status.busy": "2024-09-26T14:51:04.913094Z", + "iopub.status.idle": "2024-09-26T14:51:05.388394Z", + "shell.execute_reply": "2024-09-26T14:51:05.387783Z" } }, "outputs": [ @@ -1874,10 +1874,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:00.998663Z", - "iopub.status.busy": "2024-09-06T19:37:00.998226Z", - "iopub.status.idle": "2024-09-06T19:37:01.014613Z", - "shell.execute_reply": "2024-09-06T19:37:01.014119Z" + "iopub.execute_input": "2024-09-26T14:51:05.390453Z", + "iopub.status.busy": "2024-09-26T14:51:05.390012Z", + "iopub.status.idle": "2024-09-26T14:51:05.406956Z", + "shell.execute_reply": "2024-09-26T14:51:05.406349Z" } }, "outputs": [ @@ -2034,10 +2034,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.016951Z", - "iopub.status.busy": "2024-09-06T19:37:01.016496Z", - "iopub.status.idle": "2024-09-06T19:37:01.022189Z", - "shell.execute_reply": "2024-09-06T19:37:01.021616Z" + "iopub.execute_input": "2024-09-26T14:51:05.408881Z", + "iopub.status.busy": "2024-09-26T14:51:05.408597Z", + "iopub.status.idle": "2024-09-26T14:51:05.415372Z", + "shell.execute_reply": "2024-09-26T14:51:05.414805Z" }, "nbsphinx": "hidden" }, @@ -2082,10 +2082,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.024335Z", - "iopub.status.busy": "2024-09-06T19:37:01.024003Z", - "iopub.status.idle": "2024-09-06T19:37:01.818216Z", - "shell.execute_reply": "2024-09-06T19:37:01.817601Z" + "iopub.execute_input": "2024-09-26T14:51:05.417178Z", + "iopub.status.busy": "2024-09-26T14:51:05.416863Z", + "iopub.status.idle": "2024-09-26T14:51:06.140962Z", + "shell.execute_reply": "2024-09-26T14:51:06.140485Z" } }, "outputs": [ @@ -2167,10 +2167,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.821086Z", - "iopub.status.busy": "2024-09-06T19:37:01.820573Z", - "iopub.status.idle": "2024-09-06T19:37:01.831141Z", - "shell.execute_reply": "2024-09-06T19:37:01.830605Z" + "iopub.execute_input": "2024-09-26T14:51:06.143001Z", + "iopub.status.busy": "2024-09-26T14:51:06.142640Z", + "iopub.status.idle": "2024-09-26T14:51:06.151972Z", + "shell.execute_reply": "2024-09-26T14:51:06.151393Z" } }, "outputs": [ @@ -2298,10 +2298,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.833972Z", - "iopub.status.busy": "2024-09-06T19:37:01.833571Z", - "iopub.status.idle": "2024-09-06T19:37:01.839439Z", - "shell.execute_reply": "2024-09-06T19:37:01.838936Z" + "iopub.execute_input": "2024-09-26T14:51:06.154615Z", + "iopub.status.busy": "2024-09-26T14:51:06.153791Z", + "iopub.status.idle": "2024-09-26T14:51:06.159002Z", + "shell.execute_reply": "2024-09-26T14:51:06.158561Z" }, "nbsphinx": "hidden" }, @@ -2338,10 +2338,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:01.841836Z", - "iopub.status.busy": "2024-09-06T19:37:01.841454Z", - "iopub.status.idle": "2024-09-06T19:37:02.045788Z", - "shell.execute_reply": "2024-09-06T19:37:02.045180Z" + "iopub.execute_input": "2024-09-26T14:51:06.160864Z", + "iopub.status.busy": "2024-09-26T14:51:06.160545Z", + "iopub.status.idle": "2024-09-26T14:51:06.329043Z", + "shell.execute_reply": "2024-09-26T14:51:06.328546Z" } }, "outputs": [ @@ -2383,10 +2383,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.048026Z", - "iopub.status.busy": "2024-09-06T19:37:02.047682Z", - "iopub.status.idle": "2024-09-06T19:37:02.055980Z", - "shell.execute_reply": "2024-09-06T19:37:02.055509Z" + "iopub.execute_input": "2024-09-26T14:51:06.331161Z", + "iopub.status.busy": "2024-09-26T14:51:06.330733Z", + "iopub.status.idle": "2024-09-26T14:51:06.339030Z", + "shell.execute_reply": "2024-09-26T14:51:06.338452Z" } }, "outputs": [ @@ -2411,47 +2411,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "

" ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2472,10 +2472,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.058027Z", - "iopub.status.busy": "2024-09-06T19:37:02.057684Z", - "iopub.status.idle": "2024-09-06T19:37:02.256206Z", - "shell.execute_reply": "2024-09-06T19:37:02.255652Z" + "iopub.execute_input": "2024-09-26T14:51:06.340715Z", + "iopub.status.busy": "2024-09-26T14:51:06.340434Z", + "iopub.status.idle": "2024-09-26T14:51:06.519948Z", + "shell.execute_reply": "2024-09-26T14:51:06.519447Z" } }, "outputs": [ @@ -2515,10 +2515,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:02.258543Z", - "iopub.status.busy": "2024-09-06T19:37:02.258213Z", - "iopub.status.idle": "2024-09-06T19:37:02.262761Z", - "shell.execute_reply": "2024-09-06T19:37:02.262194Z" + "iopub.execute_input": "2024-09-26T14:51:06.522000Z", + "iopub.status.busy": "2024-09-26T14:51:06.521573Z", + "iopub.status.idle": "2024-09-26T14:51:06.526439Z", + "shell.execute_reply": "2024-09-26T14:51:06.525860Z" }, "nbsphinx": "hidden" }, @@ -2550,12 +2550,95 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "024f58b175f14cdca925ad2ec59e5f75": { + "01a52525efc245dbbd179fb0a46c9b73": { + "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": "" + } + }, + "047ffcbda41946e19a08ad3b61bf841f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_39045bc38857424e8dd68bde536fe596", + "placeholder": "​", + "style": "IPY_MODEL_edbabb45d56b40b7a43858d7ec51dcae", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "0944754e93274569967afe6d608ae5bd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0ec72a2a0065496b8b3b6ff239cb643d": { + "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_22cbf5fe8ee943da90f0599eabdc47e9", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1fd121ab479e40598a964ef1fe640df7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "0fc0d5f5c5fd4be8b001f46a67af29e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2608,25 +2691,33 @@ "width": null } }, - "0445112b7e674aa2a14ea027dc8bc2f8": { + "0fefa1fafea24c19b8f8733109f29d90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_be38efba8d3b4a918b3d7486ef513b34", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a6efe50b16d4bb4b79ecb580262a865", + "tabbable": null, + "tooltip": null, + "value": 2.0 } }, - "06c85507cf49495584b002e6aaa044e8": { + "10f1631d161c449eb8c661cb78376e34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2644,30 +2735,25 @@ "text_color": null } }, - "06fc43e80ab4403ca27ca8d667aca1b3": { + "122c68d343034072a896523a1a6f59da": { "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_4e22cd2b18d5465c8bfc301a968400ec", - "placeholder": "​", - "style": "IPY_MODEL_0445112b7e674aa2a14ea027dc8bc2f8", - "tabbable": null, - "tooltip": null, - "value": "Generating test split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "076942f2bb5542e9a9c126f736c4b427": { + "141a122f5b984ea4b8d354438a45f4ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2720,25 +2806,7 @@ "width": null } }, - "07c128e462924e039bd30ffd94caeafe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0a3d18201bb14d5c9e73af43adbe2cd8": { + "157b0b5de92c4dd39861100e0048c0b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2753,57 +2821,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e6d9313a802d4513bc93abf9ffa9fc9b", - "IPY_MODEL_6d3f4ee5439044088f65368ef798b3b6", - "IPY_MODEL_351c45295ff8422c8718ee4bdefa510f" + "IPY_MODEL_46affc3b26cb4f05ba2a6b4d66e4a233", + "IPY_MODEL_e504d10254784e0fb8866b5539c1da25", + "IPY_MODEL_5e794a3967d7453396cb620ce1a5277f" ], - "layout": "IPY_MODEL_7c5730df719649e6ae1137849667983e", + "layout": "IPY_MODEL_b707e815ff5841ea8e23fc04fa7ff454", "tabbable": null, "tooltip": null } }, - "0a606b97ecff4d89be8f66714f909ba3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "0b9bdabf441e4113805b54ee83d92f75": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_34e238c0c2c24406adb5d978aec7e807", - "placeholder": "​", - "style": "IPY_MODEL_511e7568ac814ea8846c93d586063e8e", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 285192.56 examples/s]" - } - }, - "0c2a412a844140bd80a53f2ac3fc325d": { + "1af334042da749e9a2368c61aae4462f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2856,33 +2883,41 @@ "width": null } }, - "0f5233a082d94dddbdb0503eb9250ed4": { + "1af3d41b10be4701a4e58df18c1a38f8": { "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_024f58b175f14cdca925ad2ec59e5f75", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c0366a87be804af7bcf6f5cd7f11bc3b", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1b540d6d72a748d0ac6d30324dc37e51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0f970e1142174368bfc637a6cd8d6fd5": { + "1ee09afeb3644ffaa25df19feb6d7756": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2897,15 +2932,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_881dec995ffa41c3b5cbe3a2f2955ed3", + "layout": "IPY_MODEL_a199cd7e7b8c46dea72b37e895857807", "placeholder": "​", - "style": "IPY_MODEL_7500e8476ee64140b7338f73ff7b6e53", + "style": "IPY_MODEL_d0d8518db2324e00900b4cc97bf83cba", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": " 10000/10000 [00:00<00:00, 246828.30 examples/s]" } }, - "10a3911ad0ae43f599855a0ad46d4195": { + "1f4aff5948cf4d0186015296605c7196": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2958,7 +2993,41 @@ "width": null } }, - "11645e47817743048239554d8f897a74": { + "1fd121ab479e40598a964ef1fe640df7": { + "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": "" + } + }, + "20c8eedf90bb40d392876c954589ff68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "22cbf5fe8ee943da90f0599eabdc47e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3011,7 +3080,7 @@ "width": null } }, - "118d13a1737e460b986120e1cd8488c6": { + "23438682b36c4a25982847f0236795a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3064,49 +3133,25 @@ "width": null } }, - "11c48316135a461ea55c3d08dc541755": { + "26577822b8d84e4f976ede8bcd034275": { "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d54af3f6955d4b968858d238a0210190", - "max": 9015.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f2fa408e34274722bd36f3791c967fb2", - "tabbable": null, - "tooltip": null, - "value": 9015.0 - } - }, - "13063e602ee246eb9b552b3b781fa85e": { - "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", + "_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 } }, - "1510cd85e1a74691abd66fcc8f87c34c": { + "27d69304db224bf69131404f9ec805dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3159,60 +3204,25 @@ "width": null } }, - "156d3569cd004246a2f548957d78f2bc": { - "model_module": "@jupyter-widgets/base", + "2a62f69b0c2547d89c82d6582f304d32": { + "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 } }, - "15c77a61cbac476e99fb0331858d1d8c": { + "2a6efe50b16d4bb4b79ecb580262a865": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3228,54 +3238,100 @@ "description_width": "" } }, - "1658cd683cbd496c9ff193ba8d7c35ea": { + "2b2f57b523f849bbb599cc612b663ba6": { "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_b8a497f4724b458c8448b91e3ce44d15", - "placeholder": "​", - "style": "IPY_MODEL_3c640d11994b4372834d50a9621d95c5", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:51<00:00, 1138.50it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "16923bdba0af47908931030b52eaedca": { + "2f87fcdad766443d8c4bca6afb07dd5a": { "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 + } + }, + "309f00f07977438688cb2e2a138942ee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "31aedd70a4c245c99b7449fba431ce39": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "37cb8925a1ba4ade99a9002b6b26e8df": { + "model_module": "@jupyter-widgets/controls", + "model_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_06fc43e80ab4403ca27ca8d667aca1b3", - "IPY_MODEL_0f5233a082d94dddbdb0503eb9250ed4", - "IPY_MODEL_8efceb1c08634d06817a3fa57d1a8f06" - ], - "layout": "IPY_MODEL_a9b1da96fea74c509d14483d998a7cf8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_86ca6bb1495e4902b7bab635f193c7b5", + "placeholder": "​", + "style": "IPY_MODEL_c8b6019d0f094ef6b63a722264c996a6", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 61.07it/s]" } }, - "1e5e214067f448fe820c272b4d8b60b6": { + "3814212d72544fd898cb1f84a4da144b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3291,17 +3347,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_929f19e8f15344c999957b2ce7569264", - "max": 40.0, + "layout": "IPY_MODEL_a7d9118a0a624247afaec6d0c70354df", + "max": 9015.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6c95b5f8fda84c369cae7cba5624ccfe", + "style": "IPY_MODEL_01a52525efc245dbbd179fb0a46c9b73", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 9015.0 } }, - "210dbd38b6ce4f9e9f8b810ac64d03bf": { + "38e12e1caf544c0db7e643ca4333c637": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3317,7 +3373,7 @@ "description_width": "" } }, - "23d399d3b46a4f8d82116059129fd43f": { + "39045bc38857424e8dd68bde536fe596": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3370,7 +3426,7 @@ "width": null } }, - "25c0e4e85ebb41299102ad0b3e0880b4": { + "40c57ff589b84713af0069686d77f3b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3423,30 +3479,7 @@ "width": null } }, - "26b545a844a84c278f68d51645f7e371": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ac970746b6684c3ba6bb43eee4014e2b", - "placeholder": "​", - "style": "IPY_MODEL_c77f86be94734e2ba274bf4267c5a824", - "tabbable": null, - "tooltip": null, - "value": " 2/2 [00:00<00:00, 680.45it/s]" - } - }, - "294184439d7d474cbfc6043c1efa9d3d": { + "41752bd19eff4ad7bd7f964db91e4397": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3499,7 +3532,7 @@ "width": null } }, - "29ace475fdbe4624840332dd0e509ed6": { + "43636151bcde46648bb7aaa89ccb8a51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3552,81 +3585,30 @@ "width": null } }, - "2b755b9c572e43a396e62c74fba5329f": { + "438f1faf1add4ca0bf523ed0c6de324e": { "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_11645e47817743048239554d8f897a74", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7c13df622dfc4f04853781143737296f", + "layout": "IPY_MODEL_7a0e1f1ff0db4d01b7ee26a773a3e838", + "placeholder": "​", + "style": "IPY_MODEL_122c68d343034072a896523a1a6f59da", "tabbable": null, "tooltip": null, - "value": 5175617.0 - } - }, - "3273abc0b1474d17ad8e620a0b9cd685": { - "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_a53594c87ee944d2ab253fdeb3aeae5f", - "IPY_MODEL_e38940706f02447996928468d3f523eb", - "IPY_MODEL_e127093a41b442febda97da50e709395" - ], - "layout": "IPY_MODEL_d9bfaf958ae54bdaa41267876482d6af", - "tabbable": null, - "tooltip": null - } - }, - "328179309f4646028e9f8909eefb6c74": { - "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_471bce1ec58f4d4da4ed7ed290449c5f", - "IPY_MODEL_a8fc249e1ca44d4084ed4e8e978d6058", - "IPY_MODEL_f03a0d23baa4409abb0c7271bd76ab8a" - ], - "layout": "IPY_MODEL_bb2fc960507949aea6439fcd3c77de5b", - "tabbable": null, - "tooltip": null + "value": "Downloading readme: 100%" } }, - "33d5aee8319348e485ec3980bc726f23": { + "442d008a589d43729e86f45feea617a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3679,7 +3661,7 @@ "width": null } }, - "34b3d7273cdd4ceca25a11fbb32359ce": { + "44350a751399489a9b7ac94b4aa0544e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3732,60 +3714,33 @@ "width": null } }, - "34e238c0c2c24406adb5d978aec7e807": { - "model_module": "@jupyter-widgets/base", + "4472b003fd7d40f4b054d6725929c2e0": { + "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_6c8fe2efdded46d4846420593a00bcd9", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_acc0b8fed3ec45c2b61e4cbdd87c0d3f", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "351c45295ff8422c8718ee4bdefa510f": { + "466806dd912b4f9da060546451df881b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3800,15 +3755,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8e65bd8055fd45d8999b608481497477", + "layout": "IPY_MODEL_5171002b8b9b40e58bc90a21fd7d4fce", "placeholder": "​", - "style": "IPY_MODEL_82b18a92ec124a52a6892b31409db80e", + "style": "IPY_MODEL_a1491d109915441ca6418a110cd3b606", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 64.40it/s]" + "value": "100%" } }, - "3c640d11994b4372834d50a9621d95c5": { + "4685982b10924f9aaf548d58e65e7fff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3826,100 +3781,112 @@ "text_color": null } }, - "468f054b84de4a46abae17b5d6030a66": { + "46affc3b26cb4f05ba2a6b4d66e4a233": { "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_49329574ba97425f8b3b12f07b8d53cf", - "IPY_MODEL_2b755b9c572e43a396e62c74fba5329f", - "IPY_MODEL_861644c40333498094e62f8ac990f5a3" - ], - "layout": "IPY_MODEL_0c2a412a844140bd80a53f2ac3fc325d", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1af334042da749e9a2368c61aae4462f", + "placeholder": "​", + "style": "IPY_MODEL_8fbebe50e8c94c68b5af42bcaa77c458", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Downloading data: 100%" } }, - "471bce1ec58f4d4da4ed7ed290449c5f": { + "46d058b0dc32483b92ffc747203adfbb": { "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_d9b3afcffed047abb3d7c9215eacb041", - "placeholder": "​", - "style": "IPY_MODEL_d0dfbf919e5f422689492055a00be836", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "49329574ba97425f8b3b12f07b8d53cf": { + "48ba164541954d109266e55290018b2b": { "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_77caecf6cce84ac885a2c431ec321e76", - "placeholder": "​", - "style": "IPY_MODEL_df804279770c4bbdbba569e996a72047", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cb02675b1ffa460abaf2766e247e9e76", + "IPY_MODEL_f5bdc90f13ac4085a6c87c3f16c1db23", + "IPY_MODEL_8e5227fef7bb4af398c777c479c081d2" + ], + "layout": "IPY_MODEL_442d008a589d43729e86f45feea617a0", "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" + "tooltip": null } }, - "4a900a7bd2894dc2905c92a999845c41": { + "49a7ec98f7764b44b81eb30169e07a30": { "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_78ec8846971b49dc8ac35c9865ab7855", - "placeholder": "​", - "style": "IPY_MODEL_a1e0fb35ccfd46ac9b640c1e3a97a83e", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cf2c49458b23448bbd90dd22355dc406", + "IPY_MODEL_0fefa1fafea24c19b8f8733109f29d90", + "IPY_MODEL_e1863acf55ff485abde9e3dd42efb403" + ], + "layout": "IPY_MODEL_6f45d2ba1fe14a26b1dd8d8cde2b3404", "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 58.88it/s]" + "tooltip": null + } + }, + "49ff26f36c4046ccaa23af5a0e1bd79a": { + "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": "" } }, - "4e22cd2b18d5465c8bfc301a968400ec": { + "4afd56e1be684a4ea49cf9bd94feb822": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3972,7 +3939,74 @@ "width": null } }, - "4e5be3b38c73499194dd5bfcb00e9476": { + "4c60fd9ec0a14e40806e9a0ac5d515de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4d78c4c4290346e58aeff13d1e2863f2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e1f00c2ba7fa42879c2ae82deb9ae36b", + "placeholder": "​", + "style": "IPY_MODEL_10f1631d161c449eb8c661cb78376e34", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 62.27it/s]" + } + }, + "4e00ed69c8994d9990e59ec728ec2a31": { + "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_e5d17f8bb48c489d84f85f8b0e82285e", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2b2f57b523f849bbb599cc612b663ba6", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "4e60241fc8fc4199a601cbd4c617c5e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4025,7 +4059,30 @@ "width": null } }, - "50780f5c92b44525bf711232bc998378": { + "4f30a386d77a45318659e949f29dd3b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b1371d623aa5489ca6e8e84cd59f53d7", + "placeholder": "​", + "style": "IPY_MODEL_31aedd70a4c245c99b7449fba431ce39", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "5171002b8b9b40e58bc90a21fd7d4fce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4078,25 +4135,31 @@ "width": null } }, - "511e7568ac814ea8846c93d586063e8e": { + "517b83c613bb49c9ab0cd319caf77fa4": { "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_438f1faf1add4ca0bf523ed0c6de324e", + "IPY_MODEL_3814212d72544fd898cb1f84a4da144b", + "IPY_MODEL_bf819d4aebc746d595ddad41e330f6dd" + ], + "layout": "IPY_MODEL_959554efc55740f0a1054b9bb02b35ab", + "tabbable": null, + "tooltip": null } }, - "5264eae69a4a44c5af270b7caaa7eeb4": { + "52f3a03aa4994380b8b138695e6993e3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4149,54 +4212,7 @@ "width": null } }, - "58bd914194a245c2b1a963606103a9bd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_65a24ce68aaf46d6b97c06a7d9ffc735", - "placeholder": "​", - "style": "IPY_MODEL_ed24804748ab483289fec871eb4e7ebf", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 65.43it/s]" - } - }, - "593399f7ed16479cabf5d6887e2046b5": { - "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_9db52ec22ade4fc2a207b214ed54d964", - "IPY_MODEL_7774247051444f258f5cb5a4624a6d83", - "IPY_MODEL_f082a4eab5444b019ea911ae0fb7a92d" - ], - "layout": "IPY_MODEL_8a312c718675404cb5eaf36ae41d943d", - "tabbable": null, - "tooltip": null - } - }, - "5bc3a1038e00432097a539e27f83e00f": { + "5407b52abb3543878bac58e171136ce5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4249,10 +4265,112 @@ "width": null } }, - "5cf6c3b877784057a47d544871ab0987": { + "545ab14d8bdc4758954170fd6a6c7f2e": { "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": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d16d5292876e41bbb948c7172eb1b022", + "placeholder": "​", + "style": "IPY_MODEL_9d21835857024eaca20e6c75bc8f65eb", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "54d86e58cead41e6ba4d5f91406ed1b6": { + "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 + } + }, + "554fe55159064f65be3faeef3e587efa": { + "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_44350a751399489a9b7ac94b4aa0544e", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_68ce90ceb8c5418fa6b01cf5ea89177d", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "5568c1b377ee44e2baadcd2713472be4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", @@ -4267,7 +4385,7 @@ "text_color": null } }, - "5e5e4490daf942669f04f85596a7308d": { + "5a0315cb61bf4e1b9c52127d29ddf405": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4282,15 +4400,104 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_29ace475fdbe4624840332dd0e509ed6", + "layout": "IPY_MODEL_fd18df91f07f4dd2951fcd6f5d643d2e", "placeholder": "​", - "style": "IPY_MODEL_f784e45cbe9248ae9c16491028d6bf8e", + "style": "IPY_MODEL_2a62f69b0c2547d89c82d6582f304d32", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 64.27it/s]" + } + }, + "5a31bc5cecd243f3b3fc5adfc57abc50": { + "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": "" + } + }, + "5e114e4103d94679910f2192af574f94": { + "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_c2007b76905c425eb8c5793d5064dd23", + "IPY_MODEL_eb1eb741913649deae955849ad2ff5e8", + "IPY_MODEL_8bf2df37d8174d2faa0d068cc68a2fef" + ], + "layout": "IPY_MODEL_dd4c58e0fd6948c49326bb0bb920e8c2", + "tabbable": null, + "tooltip": null + } + }, + "5e37b90b12544b039698dff9bf1b1167": { + "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_b832eb74e64e4a219cb1a39e71503899", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6cad31ec6e694b91b3e194e785d6cc13", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "5e794a3967d7453396cb620ce1a5277f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_aee54ae8933b42d09ef6de4b7c8e9cee", + "placeholder": "​", + "style": "IPY_MODEL_919e7f7fa04b447e977f5978a6008575", + "tabbable": null, + "tooltip": null, + "value": " 30.9M/30.9M [00:00<00:00, 48.5MB/s]" } }, - "606a0ff67cfd457c88c691c81de63a4c": { + "5f57350bd59941d3b3be376c1389de24": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4308,7 +4515,7 @@ "text_color": null } }, - "60b6605a27b343f3a046b38e2ee92eb3": { + "60160531292f49f6912a5e7fa5c1cd4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4323,39 +4530,94 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f9f45d26f74148d5aafa521c2e42894d", - "IPY_MODEL_f11e1a00f1c942a080552a095321e730", - "IPY_MODEL_b323c9ad204c424e81ad897a8a41faa8" + "IPY_MODEL_047ffcbda41946e19a08ad3b61bf841f", + "IPY_MODEL_4472b003fd7d40f4b054d6725929c2e0", + "IPY_MODEL_bdf9a01c9a4d4a6186410cdd21003fa3" ], - "layout": "IPY_MODEL_f1c64d058ec34988a144f78b8ce7cbc8", + "layout": "IPY_MODEL_27d69304db224bf69131404f9ec805dd", "tabbable": null, "tooltip": null } }, - "612a34311c13432a923b885221f461b0": { + "60b1a0d2d540411383054bcc09a9902a": { "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 + } + }, + "62dcb057ad134472b2f7163715a51417": { + "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_156d3569cd004246a2f548957d78f2bc", - "placeholder": "​", - "style": "IPY_MODEL_0a606b97ecff4d89be8f66714f909ba3", + "layout": "IPY_MODEL_23438682b36c4a25982847f0236795a6", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a31bc5cecd243f3b3fc5adfc57abc50", "tabbable": null, "tooltip": null, - "value": "Generating train split: 100%" + "value": 5175617.0 + } + }, + "63fd06eb481e40ff939a4af2d7ec11e1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "68ce90ceb8c5418fa6b01cf5ea89177d": { + "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": "" } }, - "62625d830bc54505aa74a2a30ef3af9d": { + "6bad8602531348f08c403c73528818d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4408,25 +4670,7 @@ "width": null } }, - "63223699d1124f63b67f209182bf8e11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "65a24ce68aaf46d6b97c06a7d9ffc735": { + "6c8fe2efdded46d4846420593a00bcd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4479,7 +4723,23 @@ "width": null } }, - "6739176497a24677bed9ce1d499ce111": { + "6cad31ec6e694b91b3e194e785d6cc13": { + "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": "" + } + }, + "6f45d2ba1fe14a26b1dd8d8cde2b3404": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4532,7 +4792,31 @@ "width": null } }, - "67956635d769462da5b0f8ec7ca4575b": { + "77e4c9ec41b8452c8963af0b77b5555f": { + "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_8838da60b00243caa8a1c207f1f8be22", + "IPY_MODEL_b934aa90c70b4cfdae426a9ca50e61a4", + "IPY_MODEL_793b99f043774f62a311dedf0c93aba0" + ], + "layout": "IPY_MODEL_83e0553f47d84eca8ffc9341907dd047", + "tabbable": null, + "tooltip": null + } + }, + "793b99f043774f62a311dedf0c93aba0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4547,15 +4831,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_34b3d7273cdd4ceca25a11fbb32359ce", + "layout": "IPY_MODEL_ce14eb0a9f94406f9670f2a1dc1345c7", "placeholder": "​", - "style": "IPY_MODEL_07c128e462924e039bd30ffd94caeafe", + "style": "IPY_MODEL_aafc8b9a1a3a4506b5483ae08647da91", "tabbable": null, "tooltip": null, - "value": "Map (num_proc=4): 100%" + "value": " 60000/60000 [00:00<00:00, 282979.24 examples/s]" } }, - "69af7b2b232142ffa08b9e4439628311": { + "7a0e1f1ff0db4d01b7ee26a773a3e838": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4608,107 +4892,60 @@ "width": null } }, - "6c5276f1cdeb4d6dafd955e313dfb495": { - "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": "" - } - }, - "6c95b5f8fda84c369cae7cba5624ccfe": { - "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": "" - } - }, - "6d3f4ee5439044088f65368ef798b3b6": { - "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_10a3911ad0ae43f599855a0ad46d4195", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ac4bae02a5884435aa084f8524ec36ab", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "6dd2d74eb1d04d61844ec3c03149c90b": { - "model_module": "@jupyter-widgets/controls", + "7de683e49bbf4f65959e300da53047ed": { + "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": "" - } - }, - "6e84e471535f4aa89081faaaa485d6c3": { - "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_eec22da0f1c144399d3b96c5a790810e", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_747540cae7c946758cd31d80531063d5", - "tabbable": null, - "tooltip": null, - "value": 60000.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6e8d203ea1f24d86a8504e8c8f549098": { + "7ef0c36f65034c0f9d5b161441b6e47c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4723,15 +4960,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6fdd97c1ed34454aa0a60e98a62224d2", + "layout": "IPY_MODEL_c91d42b9d1c342cf8093443f4eaa5204", "placeholder": "​", - "style": "IPY_MODEL_9bec32080a844b95be00f98699eec0a5", + "style": "IPY_MODEL_20c8eedf90bb40d392876c954589ff68", "tabbable": null, "tooltip": null, - "value": " 9.02k/9.02k [00:00<00:00, 1.14MB/s]" + "value": "Generating test split: 100%" } }, - "6f0a00d7d264477684a40569e5e3fb89": { + "83e0553f47d84eca8ffc9341907dd047": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4784,7 +5021,7 @@ "width": null } }, - "6f4b003f65a3475b87ea4dfb49e22177": { + "86ca6bb1495e4902b7bab635f193c7b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4837,76 +5074,123 @@ "width": null } }, - "6fdd97c1ed34454aa0a60e98a62224d2": { - "model_module": "@jupyter-widgets/base", + "8838da60b00243caa8a1c207f1f8be22": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e34ca479b1cd4338a1b034c9bdb6cdde", + "placeholder": "​", + "style": "IPY_MODEL_5f57350bd59941d3b3be376c1389de24", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" + } + }, + "8bf2df37d8174d2faa0d068cc68a2fef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9a129d278f5249b2ba66adbc4c7981b4", + "placeholder": "​", + "style": "IPY_MODEL_cbb874850a0f4b8585830f781d02f6c7", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 6394.51 examples/s]" + } + }, + "8c99cd03c2204dd69d220e1911ef407b": { + "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_4f30a386d77a45318659e949f29dd3b0", + "IPY_MODEL_e0c002bd6e8c4598837703a0b54dfdd7", + "IPY_MODEL_37cb8925a1ba4ade99a9002b6b26e8df" + ], + "layout": "IPY_MODEL_5407b52abb3543878bac58e171136ce5", + "tabbable": null, + "tooltip": null + } + }, + "8e5227fef7bb4af398c777c479c081d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_40c57ff589b84713af0069686d77f3b8", + "placeholder": "​", + "style": "IPY_MODEL_1b540d6d72a748d0ac6d30324dc37e51", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:53<00:00, 1198.28it/s]" } }, - "747540cae7c946758cd31d80531063d5": { + "8f801276525d4e939b2f6a4ae43acb97": { "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_dd7812d3359f4747a8d626fa5a9094fb", + "placeholder": "​", + "style": "IPY_MODEL_26577822b8d84e4f976ede8bcd034275", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 59.80it/s]" } }, - "7500e8476ee64140b7338f73ff7b6e53": { + "8fbebe50e8c94c68b5af42bcaa77c458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4924,33 +5208,25 @@ "text_color": null } }, - "7774247051444f258f5cb5a4624a6d83": { + "919e7f7fa04b447e977f5978a6008575": { "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_23d399d3b46a4f8d82116059129fd43f", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_13063e602ee246eb9b552b3b781fa85e", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "77caecf6cce84ac885a2c431ec321e76": { + "959554efc55740f0a1054b9bb02b35ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5003,7 +5279,7 @@ "width": null } }, - "78ec8846971b49dc8ac35c9865ab7855": { + "9a129d278f5249b2ba66adbc4c7981b4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5056,25 +5332,30 @@ "width": null } }, - "7acec2eb1b9b4c74860f49bf17a12246": { + "9b43cbe4d995486aa4d260f3e1778a5b": { "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_ccdd8d1027864d7697480a9f400c528c", + "placeholder": "​", + "style": "IPY_MODEL_2f87fcdad766443d8c4bca6afb07dd5a", + "tabbable": null, + "tooltip": null, + "value": " 5.18M/5.18M [00:00<00:00, 35.9MB/s]" } }, - "7bcf07287e5846bcade12829a0129e5a": { + "9c97151f1d7f4a49a3e2278cddb3c604": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5089,32 +5370,60 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0f970e1142174368bfc637a6cd8d6fd5", - "IPY_MODEL_11c48316135a461ea55c3d08dc541755", - "IPY_MODEL_6e8d203ea1f24d86a8504e8c8f549098" + "IPY_MODEL_fb35e5a84e5942e59aa0f8e4a6d4b045", + "IPY_MODEL_0ec72a2a0065496b8b3b6ff239cb643d", + "IPY_MODEL_4d78c4c4290346e58aeff13d1e2863f2" ], - "layout": "IPY_MODEL_6f4b003f65a3475b87ea4dfb49e22177", + "layout": "IPY_MODEL_e2ddf40e09984deca4602e0c72e8bfd6", "tabbable": null, "tooltip": null } }, - "7c13df622dfc4f04853781143737296f": { + "9d21835857024eaca20e6c75bc8f65eb": { "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 + } + }, + "9d4ba4757b8d48bba46623ad8f4b39a3": { + "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_54d86e58cead41e6ba4d5f91406ed1b6", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_38e12e1caf544c0db7e643ca4333c637", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "7c5730df719649e6ae1137849667983e": { + "a02cbe5a9ae843ff95023851f56408fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5167,7 +5476,25 @@ "width": null } }, - "7d5c3f6e3cdc47378b7a095dc828c708": { + "a1491d109915441ca6418a110cd3b606": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a199cd7e7b8c46dea72b37e895857807": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5220,30 +5547,7 @@ "width": null } }, - "7e624075712f49aeb75f702d9f7850d8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5264eae69a4a44c5af270b7caaa7eeb4", - "placeholder": "​", - "style": "IPY_MODEL_dac3e32ab9a846e79186067c2b27a96c", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" - } - }, - "82b18a92ec124a52a6892b31409db80e": { + "a3d204cf9a5a441d9ce00a62738821f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5261,7 +5565,7 @@ "text_color": null } }, - "85965cbbe5ef40678b86b3d3f8e4fc95": { + "a4367ae0bcb046beb0aa7fa55ae59b6d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5314,54 +5618,7 @@ "width": null } }, - "85a6da0e361d4bb78dac486525795dad": { - "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_612a34311c13432a923b885221f461b0", - "IPY_MODEL_c20bdfdc755d478c8d2c59d296af1748", - "IPY_MODEL_0b9bdabf441e4113805b54ee83d92f75" - ], - "layout": "IPY_MODEL_5bc3a1038e00432097a539e27f83e00f", - "tabbable": null, - "tooltip": null - } - }, - "861644c40333498094e62f8ac990f5a3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a77a4e7f5f8140bf8475f4d847910210", - "placeholder": "​", - "style": "IPY_MODEL_5cf6c3b877784057a47d544871ab0987", - "tabbable": null, - "tooltip": null, - "value": " 5.18M/5.18M [00:00<00:00, 25.9MB/s]" - } - }, - "881dec995ffa41c3b5cbe3a2f2955ed3": { + "a7d9118a0a624247afaec6d0c70354df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5414,7 +5671,7 @@ "width": null } }, - "884d6ce901e24a3797e35af5711b0f35": { + "aafc8b9a1a3a4506b5483ae08647da91": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5432,7 +5689,23 @@ "text_color": null } }, - "8a312c718675404cb5eaf36ae41d943d": { + "acc0b8fed3ec45c2b61e4cbdd87c0d3f": { + "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": "" + } + }, + "aee54ae8933b42d09ef6de4b7c8e9cee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5485,7 +5758,7 @@ "width": null } }, - "8cf5382c91f64f789a1af9c7918f14bb": { + "afdc4d351896438cb7e9760348393ddc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5500,15 +5773,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d5d9319b3ee0495d88f51937931cd00c", + "layout": "IPY_MODEL_6bad8602531348f08c403c73528818d1", "placeholder": "​", - "style": "IPY_MODEL_af8db4b8467844b8be9927dab8c5e3d9", + "style": "IPY_MODEL_0944754e93274569967afe6d608ae5bd", "tabbable": null, "tooltip": null, "value": "100%" } }, - "8e65bd8055fd45d8999b608481497477": { + "b1371d623aa5489ca6e8e84cd59f53d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5551,40 +5824,17 @@ "min_height": null, "min_width": null, "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8efceb1c08634d06817a3fa57d1a8f06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_85965cbbe5ef40678b86b3d3f8e4fc95", - "placeholder": "​", - "style": "IPY_MODEL_d86fc4609ac5440a807e454ed938d58e", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 249921.29 examples/s]" + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "92880a6894cd410ba97664fbbbbe340e": { + "b42c916027f0431eb2b8bccc86357bf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5637,7 +5887,7 @@ "width": null } }, - "929f19e8f15344c999957b2ce7569264": { + "b707e815ff5841ea8e23fc04fa7ff454": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5690,31 +5940,7 @@ "width": null } }, - "958c94ac86804e8fbd31685a6f87d389": { - "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_ac11acf9e07f48d1af94fcdf26f8c615", - "IPY_MODEL_a671667e5adf4b9798a98eda0ac57dc8", - "IPY_MODEL_4a900a7bd2894dc2905c92a999845c41" - ], - "layout": "IPY_MODEL_4e5be3b38c73499194dd5bfcb00e9476", - "tabbable": null, - "tooltip": null - } - }, - "97578dccf99646909cc139834ab78ea9": { + "b826bec48c45487bbcae63716ac684fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5767,133 +5993,7 @@ "width": null } }, - "9bec32080a844b95be00f98699eec0a5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "9db52ec22ade4fc2a207b214ed54d964": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_62625d830bc54505aa74a2a30ef3af9d", - "placeholder": "​", - "style": "IPY_MODEL_afce32644b8041548c6a8fefb3255e26", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "9fcb012408c9489cb4882ad5d8d37ecf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a1e0fb35ccfd46ac9b640c1e3a97a83e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a53594c87ee944d2ab253fdeb3aeae5f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_50780f5c92b44525bf711232bc998378", - "placeholder": "​", - "style": "IPY_MODEL_eefff7211ed94c5b90094ff9520c50b5", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "a671667e5adf4b9798a98eda0ac57dc8": { - "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_1510cd85e1a74691abd66fcc8f87c34c", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_15c77a61cbac476e99fb0331858d1d8c", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a77a4e7f5f8140bf8475f4d847910210": { + "b832eb74e64e4a219cb1a39e71503899": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5946,7 +6046,33 @@ "width": null } }, - "a88f012a925b438fbc901a161c09cf50": { + "b934aa90c70b4cfdae426a9ca50e61a4": { + "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_1f4aff5948cf4d0186015296605c7196", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bf1593492574460ca63450ff1667241f", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "badd7c549d834045856ae809d48d9fa6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5999,33 +6125,30 @@ "width": null } }, - "a8fc249e1ca44d4084ed4e8e978d6058": { + "bdf9a01c9a4d4a6186410cdd21003fa3": { "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_076942f2bb5542e9a9c126f736c4b427", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6c5276f1cdeb4d6dafd955e313dfb495", + "layout": "IPY_MODEL_41752bd19eff4ad7bd7f964db91e4397", + "placeholder": "​", + "style": "IPY_MODEL_60b1a0d2d540411383054bcc09a9902a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 40/40 [00:00<00:00, 58.52it/s]" } }, - "a9b1da96fea74c509d14483d998a7cf8": { + "be38efba8d3b4a918b3d7486ef513b34": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6078,60 +6201,152 @@ "width": null } }, - "a9e701e6d5bf4ec2a9c900edea6104e5": { - "model_module": "@jupyter-widgets/base", + "bf1593492574460ca63450ff1667241f": { + "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": "" + } + }, + "bf819d4aebc746d595ddad41e330f6dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d89cbf3e824f490b81fa19d70eb0784a", + "placeholder": "​", + "style": "IPY_MODEL_63fd06eb481e40ff939a4af2d7ec11e1", + "tabbable": null, + "tooltip": null, + "value": " 9.02k/9.02k [00:00<00:00, 960kB/s]" + } + }, + "c01e10af9cd04c4c90430d0afbaa6da0": { + "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_466806dd912b4f9da060546451df881b", + "IPY_MODEL_5e37b90b12544b039698dff9bf1b1167", + "IPY_MODEL_8f801276525d4e939b2f6a4ae43acb97" + ], + "layout": "IPY_MODEL_ca16aad0772747da8b6cf03b6abc3643", + "tabbable": null, + "tooltip": null + } + }, + "c2007b76905c425eb8c5793d5064dd23": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a4367ae0bcb046beb0aa7fa55ae59b6d", + "placeholder": "​", + "style": "IPY_MODEL_309f00f07977438688cb2e2a138942ee", + "tabbable": null, + "tooltip": null, + "value": "Map (num_proc=4): 100%" + } + }, + "c57636ad3dc54dc0926bb56946b10ab1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b42c916027f0431eb2b8bccc86357bf7", + "placeholder": "​", + "style": "IPY_MODEL_4685982b10924f9aaf548d58e65e7fff", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 54.36it/s]" + } + }, + "c5b4b8f4759d4bac96dbdbe270c10816": { + "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 + } + }, + "c8b6019d0f094ef6b63a722264c996a6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "aba8cf45b54448a59ae5e30586981cc2": { + "c91d42b9d1c342cf8093443f4eaa5204": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6184,7 +6399,7 @@ "width": null } }, - "ac11acf9e07f48d1af94fcdf26f8c615": { + "c970c34254fb445a91f6a8570b2ee0a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6199,31 +6414,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_69af7b2b232142ffa08b9e4439628311", + "layout": "IPY_MODEL_43636151bcde46648bb7aaa89ccb8a51", "placeholder": "​", - "style": "IPY_MODEL_9fcb012408c9489cb4882ad5d8d37ecf", + "style": "IPY_MODEL_4c60fd9ec0a14e40806e9a0ac5d515de", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "ac4bae02a5884435aa084f8524ec36ab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": "Downloading data: 100%" } }, - "ac970746b6684c3ba6bb43eee4014e2b": { + "ca16aad0772747da8b6cf03b6abc3643": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6276,25 +6475,30 @@ "width": null } }, - "af8db4b8467844b8be9927dab8c5e3d9": { + "cb02675b1ffa460abaf2766e247e9e76": { "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_52f3a03aa4994380b8b138695e6993e3", + "placeholder": "​", + "style": "IPY_MODEL_46d058b0dc32483b92ffc747203adfbb", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "afce32644b8041548c6a8fefb3255e26": { + "cbb874850a0f4b8585830f781d02f6c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6312,7 +6516,31 @@ "text_color": null } }, - "b149b2726a33413c8e2fde403bed8e98": { + "ccc0d279330845b8b34f60a57e76743f": { + "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_afdc4d351896438cb7e9760348393ddc", + "IPY_MODEL_9d4ba4757b8d48bba46623ad8f4b39a3", + "IPY_MODEL_5a0315cb61bf4e1b9c52127d29ddf405" + ], + "layout": "IPY_MODEL_e23cc3c422934dabbc7fa7851f6ab787", + "tabbable": null, + "tooltip": null + } + }, + "ccdd8d1027864d7697480a9f400c528c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6365,46 +6593,31 @@ "width": null } }, - "b1625d60d8254709b2fbc8015a483069": { - "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": "" - } - }, - "b323c9ad204c424e81ad897a8a41faa8": { + "cd9cd1e986f74424b60db3510b43826d": { "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_294184439d7d474cbfc6043c1efa9d3d", - "placeholder": "​", - "style": "IPY_MODEL_63223699d1124f63b67f209182bf8e11", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7ef0c36f65034c0f9d5b161441b6e47c", + "IPY_MODEL_554fe55159064f65be3faeef3e587efa", + "IPY_MODEL_1ee09afeb3644ffaa25df19feb6d7756" + ], + "layout": "IPY_MODEL_f8bd324861dd4e2ca40f79d16ca6ca8a", "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.13it/s]" + "tooltip": null } }, - "b8a497f4724b458c8448b91e3ce44d15": { + "ce14eb0a9f94406f9670f2a1dc1345c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6457,31 +6670,48 @@ "width": null } }, - "b8c0903ec57a4db09eef7c66d76ad798": { + "cf2c49458b23448bbd90dd22355dc406": { "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_5e5e4490daf942669f04f85596a7308d", - "IPY_MODEL_c1960242d8a3445da3b5cfd12aa829f9", - "IPY_MODEL_1658cd683cbd496c9ff193ba8d7c35ea" - ], - "layout": "IPY_MODEL_6739176497a24677bed9ce1d499ce111", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0fc0d5f5c5fd4be8b001f46a67af29e6", + "placeholder": "​", + "style": "IPY_MODEL_5568c1b377ee44e2baadcd2713472be4", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "Computing checksums: 100%" + } + }, + "d0d8518db2324e00900b4cc97bf83cba": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "bb2fc960507949aea6439fcd3c77de5b": { + "d16d5292876e41bbb948c7172eb1b022": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6534,7 +6764,7 @@ "width": null } }, - "bcb6b4fdcebe40208119e7b000c67176": { + "d89cbf3e824f490b81fa19d70eb0784a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6587,99 +6817,31 @@ "width": null } }, - "bfcb4b6339d14370bc404a61e757edfd": { - "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_67956635d769462da5b0f8ec7ca4575b", - "IPY_MODEL_6e84e471535f4aa89081faaaa485d6c3", - "IPY_MODEL_f0cb3f6ef1cd478f8be08c7d0285e829" - ], - "layout": "IPY_MODEL_cc31bc3295e7426890cf527d78a13416", - "tabbable": null, - "tooltip": null - } - }, - "c0366a87be804af7bcf6f5cd7f11bc3b": { - "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": "" - } - }, - "c1960242d8a3445da3b5cfd12aa829f9": { - "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_92880a6894cd410ba97664fbbbbe340e", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b1625d60d8254709b2fbc8015a483069", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "c20bdfdc755d478c8d2c59d296af1748": { + "dc9f46273db144d982f466b186d6ea8d": { "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_6f0a00d7d264477684a40569e5e3fb89", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_210dbd38b6ce4f9e9f8b810ac64d03bf", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c970c34254fb445a91f6a8570b2ee0a6", + "IPY_MODEL_62dcb057ad134472b2f7163715a51417", + "IPY_MODEL_9b43cbe4d995486aa4d260f3e1778a5b" + ], + "layout": "IPY_MODEL_badd7c549d834045856ae809d48d9fa6", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "c65df96729f9462e8df514c9d2bab3e8": { + "dd4c58e0fd6948c49326bb0bb920e8c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6732,25 +6894,7 @@ "width": null } }, - "c77f86be94734e2ba274bf4267c5a824": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cc31bc3295e7426890cf527d78a13416": { + "dd7812d3359f4747a8d626fa5a9094fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6803,65 +6947,56 @@ "width": null } }, - "cdf6adba96a64fc5a04910695f01468c": { - "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": "" - } - }, - "cef86182d7ef449481f59dfea70aa34a": { + "e0c002bd6e8c4598837703a0b54dfdd7": { "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_8cf5382c91f64f789a1af9c7918f14bb", - "IPY_MODEL_1e5e214067f448fe820c272b4d8b60b6", - "IPY_MODEL_58bd914194a245c2b1a963606103a9bd" - ], - "layout": "IPY_MODEL_25c0e4e85ebb41299102ad0b3e0880b4", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b826bec48c45487bbcae63716ac684fb", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f241c4541bfc4e678ceec3ff46602200", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "d0dfbf919e5f422689492055a00be836": { + "e1863acf55ff485abde9e3dd42efb403": { "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_fc81a44ecf7c4ad1b851ab50894a6d71", + "placeholder": "​", + "style": "IPY_MODEL_c5b4b8f4759d4bac96dbdbe270c10816", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 584.78it/s]" } }, - "d54af3f6955d4b968858d238a0210190": { + "e1f00c2ba7fa42879c2ae82deb9ae36b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6914,7 +7049,31 @@ "width": null } }, - "d5d9319b3ee0495d88f51937931cd00c": { + "e20ec2fb456e4bb9bfb446110e53d341": { + "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_545ab14d8bdc4758954170fd6a6c7f2e", + "IPY_MODEL_4e00ed69c8994d9990e59ec728ec2a31", + "IPY_MODEL_c57636ad3dc54dc0926bb56946b10ab1" + ], + "layout": "IPY_MODEL_141a122f5b984ea4b8d354438a45f4ac", + "tabbable": null, + "tooltip": null + } + }, + "e23cc3c422934dabbc7fa7851f6ab787": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6967,51 +7126,7 @@ "width": null } }, - "d86fc4609ac5440a807e454ed938d58e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d936e9a2111644719473853bc9465d85": { - "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_aba8cf45b54448a59ae5e30586981cc2", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e06e8dd00ef946db9d9676c674e9f1ff", - "tabbable": null, - "tooltip": null, - "value": 2.0 - } - }, - "d9b3afcffed047abb3d7c9215eacb041": { + "e2ddf40e09984deca4602e0c72e8bfd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7064,7 +7179,7 @@ "width": null } }, - "d9bfaf958ae54bdaa41267876482d6af": { + "e34ca479b1cd4338a1b034c9bdb6cdde": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7111,197 +7226,39 @@ "order": null, "overflow": null, "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "da5cdaff84244e95b85f4f6729933e89": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "dac3e32ab9a846e79186067c2b27a96c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "df804279770c4bbdbba569e996a72047": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e06e8dd00ef946db9d9676c674e9f1ff": { - "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": "" - } - }, - "e127093a41b442febda97da50e709395": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7d5c3f6e3cdc47378b7a095dc828c708", - "placeholder": "​", - "style": "IPY_MODEL_7acec2eb1b9b4c74860f49bf17a12246", - "tabbable": null, - "tooltip": null, - "value": " 30.9M/30.9M [00:00<00:00, 85.2MB/s]" - } - }, - "e38940706f02447996928468d3f523eb": { - "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_97578dccf99646909cc139834ab78ea9", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cdf6adba96a64fc5a04910695f01468c", - "tabbable": null, - "tooltip": null, - "value": 30931277.0 + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "e6d9313a802d4513bc93abf9ffa9fc9b": { + "e504d10254784e0fb8866b5539c1da25": { "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_bcb6b4fdcebe40208119e7b000c67176", - "placeholder": "​", - "style": "IPY_MODEL_606a0ff67cfd457c88c691c81de63a4c", + "layout": "IPY_MODEL_4afd56e1be684a4ea49cf9bd94feb822", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1af3d41b10be4701a4e58df18c1a38f8", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "ed24804748ab483289fec871eb4e7ebf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ee5568e238c045b59cf17074e12437c9": { - "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_7e624075712f49aeb75f702d9f7850d8", - "IPY_MODEL_d936e9a2111644719473853bc9465d85", - "IPY_MODEL_26b545a844a84c278f68d51645f7e371" - ], - "layout": "IPY_MODEL_33d5aee8319348e485ec3980bc726f23", - "tabbable": null, - "tooltip": null + "value": 30931277.0 } }, - "eec22da0f1c144399d3b96c5a790810e": { + "e5d17f8bb48c489d84f85f8b0e82285e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7354,94 +7311,83 @@ "width": null } }, - "eefff7211ed94c5b90094ff9520c50b5": { + "ea25312b42fa433294a739c7e0d5c2a9": { "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": "" } }, - "f03a0d23baa4409abb0c7271bd76ab8a": { + "eb1eb741913649deae955849ad2ff5e8": { "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_c65df96729f9462e8df514c9d2bab3e8", - "placeholder": "​", - "style": "IPY_MODEL_06c85507cf49495584b002e6aaa044e8", + "layout": "IPY_MODEL_a02cbe5a9ae843ff95023851f56408fe", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ea25312b42fa433294a739c7e0d5c2a9", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 61.46it/s]" + "value": 60000.0 } }, - "f082a4eab5444b019ea911ae0fb7a92d": { + "edbabb45d56b40b7a43858d7ec51dcae": { "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_a88f012a925b438fbc901a161c09cf50", - "placeholder": "​", - "style": "IPY_MODEL_f7b0cd88615641199d9599093664c3f3", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.47it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f0cb3f6ef1cd478f8be08c7d0285e829": { + "f241c4541bfc4e678ceec3ff46602200": { "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_118d13a1737e460b986120e1cd8488c6", - "placeholder": "​", - "style": "IPY_MODEL_884d6ce901e24a3797e35af5711b0f35", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 5023.35 examples/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f11e1a00f1c942a080552a095321e730": { + "f5bdc90f13ac4085a6c87c3f16c1db23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -7457,17 +7403,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b149b2726a33413c8e2fde403bed8e98", - "max": 40.0, + "layout": "IPY_MODEL_7de683e49bbf4f65959e300da53047ed", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6dd2d74eb1d04d61844ec3c03149c90b", + "style": "IPY_MODEL_49ff26f36c4046ccaa23af5a0e1bd79a", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 } }, - "f1c64d058ec34988a144f78b8ce7cbc8": { + "f8bd324861dd4e2ca40f79d16ca6ca8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7520,79 +7466,133 @@ "width": null } }, - "f2fa408e34274722bd36f3791c967fb2": { + "fb35e5a84e5942e59aa0f8e4a6d4b045": { "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_4e60241fc8fc4199a601cbd4c617c5e6", + "placeholder": "​", + "style": "IPY_MODEL_a3d204cf9a5a441d9ce00a62738821f3", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "f784e45cbe9248ae9c16491028d6bf8e": { - "model_module": "@jupyter-widgets/controls", + "fc81a44ecf7c4ad1b851ab50894a6d71": { + "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 } }, - "f7b0cd88615641199d9599093664c3f3": { - "model_module": "@jupyter-widgets/controls", + "fd18df91f07f4dd2951fcd6f5d643d2e": { + "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 - } - }, - "f9f45d26f74148d5aafa521c2e42894d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a9e701e6d5bf4ec2a9c900edea6104e5", - "placeholder": "​", - "style": "IPY_MODEL_da5cdaff84244e95b85f4f6729933e89", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } } }, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 55a26f513..688f732c4 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": "2024-09-06T19:37:06.951842Z", - "iopub.status.busy": "2024-09-06T19:37:06.951670Z", - "iopub.status.idle": "2024-09-06T19:37:08.104160Z", - "shell.execute_reply": "2024-09-06T19:37:08.103605Z" + "iopub.execute_input": "2024-09-26T14:51:11.092091Z", + "iopub.status.busy": "2024-09-26T14:51:11.091687Z", + "iopub.status.idle": "2024-09-26T14:51:12.301495Z", + "shell.execute_reply": "2024-09-26T14:51:12.300906Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:08.106594Z", - "iopub.status.busy": "2024-09-06T19:37:08.106312Z", - "iopub.status.idle": "2024-09-06T19:37:08.124373Z", - "shell.execute_reply": "2024-09-06T19:37:08.123937Z" + "iopub.execute_input": "2024-09-26T14:51:12.303829Z", + "iopub.status.busy": "2024-09-26T14:51:12.303363Z", + "iopub.status.idle": "2024-09-26T14:51:12.322353Z", + "shell.execute_reply": "2024-09-26T14:51:12.321902Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.126574Z", - "iopub.status.busy": "2024-09-06T19:37:08.126159Z", - "iopub.status.idle": "2024-09-06T19:37:08.148467Z", - "shell.execute_reply": "2024-09-06T19:37:08.148011Z" + "iopub.execute_input": "2024-09-26T14:51:12.324450Z", + "iopub.status.busy": "2024-09-26T14:51:12.324010Z", + "iopub.status.idle": "2024-09-26T14:51:12.348557Z", + "shell.execute_reply": "2024-09-26T14:51:12.348062Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.150542Z", - "iopub.status.busy": "2024-09-06T19:37:08.150195Z", - "iopub.status.idle": "2024-09-06T19:37:08.153510Z", - "shell.execute_reply": "2024-09-06T19:37:08.153043Z" + "iopub.execute_input": "2024-09-26T14:51:12.350597Z", + "iopub.status.busy": "2024-09-26T14:51:12.350164Z", + "iopub.status.idle": "2024-09-26T14:51:12.353712Z", + "shell.execute_reply": "2024-09-26T14:51:12.353237Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.155506Z", - "iopub.status.busy": "2024-09-06T19:37:08.155162Z", - "iopub.status.idle": "2024-09-06T19:37:08.163216Z", - "shell.execute_reply": "2024-09-06T19:37:08.162658Z" + "iopub.execute_input": "2024-09-26T14:51:12.355535Z", + "iopub.status.busy": "2024-09-26T14:51:12.355193Z", + "iopub.status.idle": "2024-09-26T14:51:12.364277Z", + "shell.execute_reply": "2024-09-26T14:51:12.363833Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.165384Z", - "iopub.status.busy": "2024-09-06T19:37:08.164978Z", - "iopub.status.idle": "2024-09-06T19:37:08.167532Z", - "shell.execute_reply": "2024-09-06T19:37:08.167093Z" + "iopub.execute_input": "2024-09-26T14:51:12.366192Z", + "iopub.status.busy": "2024-09-26T14:51:12.365860Z", + "iopub.status.idle": "2024-09-26T14:51:12.368238Z", + "shell.execute_reply": "2024-09-26T14:51:12.367806Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:08.169550Z", - "iopub.status.busy": "2024-09-06T19:37:08.169205Z", - "iopub.status.idle": "2024-09-06T19:37:11.232996Z", - "shell.execute_reply": "2024-09-06T19:37:11.232340Z" + "iopub.execute_input": "2024-09-26T14:51:12.369910Z", + "iopub.status.busy": "2024-09-26T14:51:12.369584Z", + "iopub.status.idle": "2024-09-26T14:51:15.473892Z", + "shell.execute_reply": "2024-09-26T14:51:15.473328Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:11.235550Z", - "iopub.status.busy": "2024-09-06T19:37:11.235362Z", - "iopub.status.idle": "2024-09-06T19:37:11.244291Z", - "shell.execute_reply": "2024-09-06T19:37:11.243862Z" + "iopub.execute_input": "2024-09-26T14:51:15.476275Z", + "iopub.status.busy": "2024-09-26T14:51:15.475917Z", + "iopub.status.idle": "2024-09-26T14:51:15.485407Z", + "shell.execute_reply": "2024-09-26T14:51:15.484796Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:11.246379Z", - "iopub.status.busy": "2024-09-06T19:37:11.246205Z", - "iopub.status.idle": "2024-09-06T19:37:13.219249Z", - "shell.execute_reply": "2024-09-06T19:37:13.218645Z" + "iopub.execute_input": "2024-09-26T14:51:15.487349Z", + "iopub.status.busy": "2024-09-26T14:51:15.487005Z", + "iopub.status.idle": "2024-09-26T14:51:17.515517Z", + "shell.execute_reply": "2024-09-26T14:51:17.514901Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.221677Z", - "iopub.status.busy": "2024-09-06T19:37:13.221173Z", - "iopub.status.idle": "2024-09-06T19:37:13.240218Z", - "shell.execute_reply": "2024-09-06T19:37:13.239749Z" + "iopub.execute_input": "2024-09-26T14:51:17.517807Z", + "iopub.status.busy": "2024-09-26T14:51:17.517109Z", + "iopub.status.idle": "2024-09-26T14:51:17.536120Z", + "shell.execute_reply": "2024-09-26T14:51:17.535624Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.242381Z", - "iopub.status.busy": "2024-09-06T19:37:13.242042Z", - "iopub.status.idle": "2024-09-06T19:37:13.250225Z", - "shell.execute_reply": "2024-09-06T19:37:13.249765Z" + "iopub.execute_input": "2024-09-26T14:51:17.537976Z", + "iopub.status.busy": "2024-09-26T14:51:17.537611Z", + "iopub.status.idle": "2024-09-26T14:51:17.545869Z", + "shell.execute_reply": "2024-09-26T14:51:17.545319Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.252315Z", - "iopub.status.busy": "2024-09-06T19:37:13.251975Z", - "iopub.status.idle": "2024-09-06T19:37:13.260671Z", - "shell.execute_reply": "2024-09-06T19:37:13.260195Z" + "iopub.execute_input": "2024-09-26T14:51:17.547622Z", + "iopub.status.busy": "2024-09-26T14:51:17.547301Z", + "iopub.status.idle": "2024-09-26T14:51:17.556250Z", + "shell.execute_reply": "2024-09-26T14:51:17.555755Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.262712Z", - "iopub.status.busy": "2024-09-06T19:37:13.262373Z", - "iopub.status.idle": "2024-09-06T19:37:13.270531Z", - "shell.execute_reply": "2024-09-06T19:37:13.269960Z" + "iopub.execute_input": "2024-09-26T14:51:17.557888Z", + "iopub.status.busy": "2024-09-26T14:51:17.557705Z", + "iopub.status.idle": "2024-09-26T14:51:17.565685Z", + "shell.execute_reply": "2024-09-26T14:51:17.565225Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.272557Z", - "iopub.status.busy": "2024-09-06T19:37:13.272379Z", - "iopub.status.idle": "2024-09-06T19:37:13.281035Z", - "shell.execute_reply": "2024-09-06T19:37:13.280557Z" + "iopub.execute_input": "2024-09-26T14:51:17.567291Z", + "iopub.status.busy": "2024-09-26T14:51:17.567107Z", + "iopub.status.idle": "2024-09-26T14:51:17.576362Z", + "shell.execute_reply": "2024-09-26T14:51:17.575909Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.283068Z", - "iopub.status.busy": "2024-09-06T19:37:13.282889Z", - "iopub.status.idle": "2024-09-06T19:37:13.290486Z", - "shell.execute_reply": "2024-09-06T19:37:13.290023Z" + "iopub.execute_input": "2024-09-26T14:51:17.577990Z", + "iopub.status.busy": "2024-09-26T14:51:17.577812Z", + "iopub.status.idle": "2024-09-26T14:51:17.585393Z", + "shell.execute_reply": "2024-09-26T14:51:17.584817Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.292532Z", - "iopub.status.busy": "2024-09-06T19:37:13.292191Z", - "iopub.status.idle": "2024-09-06T19:37:13.299536Z", - "shell.execute_reply": "2024-09-06T19:37:13.298963Z" + "iopub.execute_input": "2024-09-26T14:51:17.587245Z", + "iopub.status.busy": "2024-09-26T14:51:17.586929Z", + "iopub.status.idle": "2024-09-26T14:51:17.594347Z", + "shell.execute_reply": "2024-09-26T14:51:17.593795Z" } }, "outputs": [ @@ -1306,10 +1306,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:13.301807Z", - "iopub.status.busy": "2024-09-06T19:37:13.301492Z", - "iopub.status.idle": "2024-09-06T19:37:13.309949Z", - "shell.execute_reply": "2024-09-06T19:37:13.309476Z" + "iopub.execute_input": "2024-09-26T14:51:17.596172Z", + "iopub.status.busy": "2024-09-26T14:51:17.595784Z", + "iopub.status.idle": "2024-09-26T14:51:17.604165Z", + "shell.execute_reply": "2024-09-26T14:51:17.603720Z" }, "nbsphinx": "hidden" }, @@ -1373,7 +1373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index b51846d10..cb95da0ec 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -795,7 +795,7 @@

2. Load and format the text dataset
 This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'change_pin'}
+Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'apple_pay_or_google_pay', '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 0357de56a..5b5c4a565 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": "2024-09-06T19:37:16.238148Z", - "iopub.status.busy": "2024-09-06T19:37:16.237968Z", - "iopub.status.idle": "2024-09-06T19:37:19.032647Z", - "shell.execute_reply": "2024-09-06T19:37:19.031997Z" + "iopub.execute_input": "2024-09-26T14:51:20.550084Z", + "iopub.status.busy": "2024-09-26T14:51:20.549919Z", + "iopub.status.idle": "2024-09-26T14:51:23.546779Z", + "shell.execute_reply": "2024-09-26T14:51:23.546140Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:19.035274Z", - "iopub.status.busy": "2024-09-06T19:37:19.034943Z", - "iopub.status.idle": "2024-09-06T19:37:19.038478Z", - "shell.execute_reply": "2024-09-06T19:37:19.037992Z" + "iopub.execute_input": "2024-09-26T14:51:23.549062Z", + "iopub.status.busy": "2024-09-26T14:51:23.548756Z", + "iopub.status.idle": "2024-09-26T14:51:23.551996Z", + "shell.execute_reply": "2024-09-26T14:51:23.551554Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.040624Z", - "iopub.status.busy": "2024-09-06T19:37:19.040295Z", - "iopub.status.idle": "2024-09-06T19:37:19.043522Z", - "shell.execute_reply": "2024-09-06T19:37:19.043021Z" + "iopub.execute_input": "2024-09-26T14:51:23.553571Z", + "iopub.status.busy": "2024-09-26T14:51:23.553396Z", + "iopub.status.idle": "2024-09-26T14:51:23.556530Z", + "shell.execute_reply": "2024-09-26T14:51:23.556072Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.045678Z", - "iopub.status.busy": "2024-09-06T19:37:19.045330Z", - "iopub.status.idle": "2024-09-06T19:37:19.065598Z", - "shell.execute_reply": "2024-09-06T19:37:19.065087Z" + "iopub.execute_input": "2024-09-26T14:51:23.558190Z", + "iopub.status.busy": "2024-09-26T14:51:23.558016Z", + "iopub.status.idle": "2024-09-26T14:51:23.584373Z", + "shell.execute_reply": "2024-09-26T14:51:23.583877Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.067819Z", - "iopub.status.busy": "2024-09-06T19:37:19.067470Z", - "iopub.status.idle": "2024-09-06T19:37:19.071077Z", - "shell.execute_reply": "2024-09-06T19:37:19.070583Z" + "iopub.execute_input": "2024-09-26T14:51:23.586327Z", + "iopub.status.busy": "2024-09-26T14:51:23.585980Z", + "iopub.status.idle": "2024-09-26T14:51:23.589627Z", + "shell.execute_reply": "2024-09-26T14:51:23.589147Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'change_pin'}\n" + "Classes: {'supported_cards_and_currencies', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'change_pin', 'beneficiary_not_allowed', 'card_about_to_expire', 'getting_spare_card', 'apple_pay_or_google_pay', 'cancel_transfer'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.073199Z", - "iopub.status.busy": "2024-09-06T19:37:19.072859Z", - "iopub.status.idle": "2024-09-06T19:37:19.075873Z", - "shell.execute_reply": "2024-09-06T19:37:19.075346Z" + "iopub.execute_input": "2024-09-26T14:51:23.591183Z", + "iopub.status.busy": "2024-09-26T14:51:23.591009Z", + "iopub.status.idle": "2024-09-26T14:51:23.594239Z", + "shell.execute_reply": "2024-09-26T14:51:23.593788Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:19.077966Z", - "iopub.status.busy": "2024-09-06T19:37:19.077636Z", - "iopub.status.idle": "2024-09-06T19:37:23.171760Z", - "shell.execute_reply": "2024-09-06T19:37:23.171196Z" + "iopub.execute_input": "2024-09-26T14:51:23.595893Z", + "iopub.status.busy": "2024-09-26T14:51:23.595586Z", + "iopub.status.idle": "2024-09-26T14:51:27.775987Z", + "shell.execute_reply": "2024-09-26T14:51:27.775330Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:23.174471Z", - "iopub.status.busy": "2024-09-06T19:37:23.174274Z", - "iopub.status.idle": "2024-09-06T19:37:24.103567Z", - "shell.execute_reply": "2024-09-06T19:37:24.102969Z" + "iopub.execute_input": "2024-09-26T14:51:27.778341Z", + "iopub.status.busy": "2024-09-26T14:51:27.777966Z", + "iopub.status.idle": "2024-09-26T14:51:28.697834Z", + "shell.execute_reply": "2024-09-26T14:51:28.697228Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:24.107438Z", - "iopub.status.busy": "2024-09-06T19:37:24.106451Z", - "iopub.status.idle": "2024-09-06T19:37:24.110626Z", - "shell.execute_reply": "2024-09-06T19:37:24.110110Z" + "iopub.execute_input": "2024-09-26T14:51:28.700329Z", + "iopub.status.busy": "2024-09-26T14:51:28.699942Z", + "iopub.status.idle": "2024-09-26T14:51:28.702874Z", + "shell.execute_reply": "2024-09-26T14:51:28.702381Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:24.114244Z", - "iopub.status.busy": "2024-09-06T19:37:24.113304Z", - "iopub.status.idle": "2024-09-06T19:37:26.122882Z", - "shell.execute_reply": "2024-09-06T19:37:26.122195Z" + "iopub.execute_input": "2024-09-26T14:51:28.704853Z", + "iopub.status.busy": "2024-09-26T14:51:28.704499Z", + "iopub.status.idle": "2024-09-26T14:51:30.723899Z", + "shell.execute_reply": "2024-09-26T14:51:30.723229Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.126146Z", - "iopub.status.busy": "2024-09-06T19:37:26.125493Z", - "iopub.status.idle": "2024-09-06T19:37:26.149493Z", - "shell.execute_reply": "2024-09-06T19:37:26.148954Z" + "iopub.execute_input": "2024-09-26T14:51:30.727734Z", + "iopub.status.busy": "2024-09-26T14:51:30.726555Z", + "iopub.status.idle": "2024-09-26T14:51:30.752360Z", + "shell.execute_reply": "2024-09-26T14:51:30.751847Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.152122Z", - "iopub.status.busy": "2024-09-06T19:37:26.151750Z", - "iopub.status.idle": "2024-09-06T19:37:26.163613Z", - "shell.execute_reply": "2024-09-06T19:37:26.163031Z" + "iopub.execute_input": "2024-09-26T14:51:30.755440Z", + "iopub.status.busy": "2024-09-26T14:51:30.754576Z", + "iopub.status.idle": "2024-09-26T14:51:30.764760Z", + "shell.execute_reply": "2024-09-26T14:51:30.764347Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.165819Z", - "iopub.status.busy": "2024-09-06T19:37:26.165507Z", - "iopub.status.idle": "2024-09-06T19:37:26.169927Z", - "shell.execute_reply": "2024-09-06T19:37:26.169445Z" + "iopub.execute_input": "2024-09-26T14:51:30.767190Z", + "iopub.status.busy": "2024-09-26T14:51:30.766574Z", + "iopub.status.idle": "2024-09-26T14:51:30.771522Z", + "shell.execute_reply": "2024-09-26T14:51:30.771112Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.171802Z", - "iopub.status.busy": "2024-09-06T19:37:26.171622Z", - "iopub.status.idle": "2024-09-06T19:37:26.178323Z", - "shell.execute_reply": "2024-09-06T19:37:26.177759Z" + "iopub.execute_input": "2024-09-26T14:51:30.773863Z", + "iopub.status.busy": "2024-09-26T14:51:30.773237Z", + "iopub.status.idle": "2024-09-26T14:51:30.780343Z", + "shell.execute_reply": "2024-09-26T14:51:30.779939Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.180430Z", - "iopub.status.busy": "2024-09-06T19:37:26.180102Z", - "iopub.status.idle": "2024-09-06T19:37:26.186371Z", - "shell.execute_reply": "2024-09-06T19:37:26.185807Z" + "iopub.execute_input": "2024-09-26T14:51:30.782231Z", + "iopub.status.busy": "2024-09-26T14:51:30.782055Z", + "iopub.status.idle": "2024-09-26T14:51:30.788970Z", + "shell.execute_reply": "2024-09-26T14:51:30.788375Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.188480Z", - "iopub.status.busy": "2024-09-06T19:37:26.188150Z", - "iopub.status.idle": "2024-09-06T19:37:26.194198Z", - "shell.execute_reply": "2024-09-06T19:37:26.193624Z" + "iopub.execute_input": "2024-09-26T14:51:30.790778Z", + "iopub.status.busy": "2024-09-26T14:51:30.790601Z", + "iopub.status.idle": "2024-09-26T14:51:30.796446Z", + "shell.execute_reply": "2024-09-26T14:51:30.795882Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.196327Z", - "iopub.status.busy": "2024-09-06T19:37:26.195981Z", - "iopub.status.idle": "2024-09-06T19:37:26.204376Z", - "shell.execute_reply": "2024-09-06T19:37:26.203913Z" + "iopub.execute_input": "2024-09-26T14:51:30.798232Z", + "iopub.status.busy": "2024-09-26T14:51:30.797967Z", + "iopub.status.idle": "2024-09-26T14:51:30.806498Z", + "shell.execute_reply": "2024-09-26T14:51:30.805933Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.206432Z", - "iopub.status.busy": "2024-09-06T19:37:26.206091Z", - "iopub.status.idle": "2024-09-06T19:37:26.211539Z", - "shell.execute_reply": "2024-09-06T19:37:26.211070Z" + "iopub.execute_input": "2024-09-26T14:51:30.808353Z", + "iopub.status.busy": "2024-09-26T14:51:30.808081Z", + "iopub.status.idle": "2024-09-26T14:51:30.813342Z", + "shell.execute_reply": "2024-09-26T14:51:30.812825Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.213685Z", - "iopub.status.busy": "2024-09-06T19:37:26.213350Z", - "iopub.status.idle": "2024-09-06T19:37:26.218528Z", - "shell.execute_reply": "2024-09-06T19:37:26.218074Z" + "iopub.execute_input": "2024-09-26T14:51:30.814997Z", + "iopub.status.busy": "2024-09-26T14:51:30.814668Z", + "iopub.status.idle": "2024-09-26T14:51:30.819982Z", + "shell.execute_reply": "2024-09-26T14:51:30.819532Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.220571Z", - "iopub.status.busy": "2024-09-06T19:37:26.220232Z", - "iopub.status.idle": "2024-09-06T19:37:26.223906Z", - "shell.execute_reply": "2024-09-06T19:37:26.223327Z" + "iopub.execute_input": "2024-09-26T14:51:30.821669Z", + "iopub.status.busy": "2024-09-26T14:51:30.821337Z", + "iopub.status.idle": "2024-09-26T14:51:30.824940Z", + "shell.execute_reply": "2024-09-26T14:51:30.824366Z" } }, "outputs": [ @@ -1449,10 +1449,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:26.226160Z", - "iopub.status.busy": "2024-09-06T19:37:26.225819Z", - "iopub.status.idle": "2024-09-06T19:37:26.231140Z", - "shell.execute_reply": "2024-09-06T19:37:26.230573Z" + "iopub.execute_input": "2024-09-26T14:51:30.826780Z", + "iopub.status.busy": "2024-09-26T14:51:30.826459Z", + "iopub.status.idle": "2024-09-26T14:51:30.831493Z", + "shell.execute_reply": "2024-09-26T14:51:30.831041Z" }, "nbsphinx": "hidden" }, @@ -1497,7 +1497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index bdff93976..ad7b0db5d 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -837,7 +837,7 @@

4. Identify Data Issues Using Datalab @@ -883,13 +883,13 @@

4. Identify Data Issues Using Datalab - +
- - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
@@ -3507,16 +3507,16 @@

1. Load the Dataset
---2024-09-06 19:37:46--  https://s.cleanlab.ai/CIFAR-10-subset.zip
+--2024-09-26 14:51:50--  https://s.cleanlab.ai/CIFAR-10-subset.zip
 Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...
 Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 986707 (964K) [application/zip]
 Saving to: ‘CIFAR-10-subset.zip’
 
-CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.005s
+CIFAR-10-subset.zip 100%[===================>] 963.58K  --.-KB/s    in 0.009s
 
-2024-09-06 19:37:46 (176 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
+2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]
 
 
@@ -3586,7 +3586,7 @@

2. Run Datalab Analysis
-
+
@@ -3930,7 +3930,7 @@

3. Interpret the ResultsFrog class (Class 0 in the plot) have been darkened, while 100 images from the Truck class (Class 1 in the plot) remain unchanged, as in the CIFAR-10 dataset. This creates a clear spurious correlation between the ‘darkness’ feature and the class labels: Frog images are dark, whereas Truck images are not. We can see that the dark_score values between the two classes are non-overlapping. This characteristic of the dataset is identified by Datalab.

diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index 0c93ce2cb..9404f1540 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": "2024-09-06T19:37:29.604724Z", - "iopub.status.busy": "2024-09-06T19:37:29.604545Z", - "iopub.status.idle": "2024-09-06T19:37:30.035194Z", - "shell.execute_reply": "2024-09-06T19:37:30.034674Z" + "iopub.execute_input": "2024-09-26T14:51:34.296488Z", + "iopub.status.busy": "2024-09-26T14:51:34.296076Z", + "iopub.status.idle": "2024-09-26T14:51:35.016105Z", + "shell.execute_reply": "2024-09-26T14:51:35.015553Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.037845Z", - "iopub.status.busy": "2024-09-06T19:37:30.037406Z", - "iopub.status.idle": "2024-09-06T19:37:30.168185Z", - "shell.execute_reply": "2024-09-06T19:37:30.167636Z" + "iopub.execute_input": "2024-09-26T14:51:35.018426Z", + "iopub.status.busy": "2024-09-26T14:51:35.017967Z", + "iopub.status.idle": "2024-09-26T14:51:35.151580Z", + "shell.execute_reply": "2024-09-26T14:51:35.151068Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.170587Z", - "iopub.status.busy": "2024-09-06T19:37:30.170087Z", - "iopub.status.idle": "2024-09-06T19:37:30.193350Z", - "shell.execute_reply": "2024-09-06T19:37:30.192776Z" + "iopub.execute_input": "2024-09-26T14:51:35.153697Z", + "iopub.status.busy": "2024-09-26T14:51:35.153277Z", + "iopub.status.idle": "2024-09-26T14:51:35.177588Z", + "shell.execute_reply": "2024-09-26T14:51:35.176982Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:30.195997Z", - "iopub.status.busy": "2024-09-06T19:37:30.195790Z", - "iopub.status.idle": "2024-09-06T19:37:32.997740Z", - "shell.execute_reply": "2024-09-06T19:37:32.997128Z" + "iopub.execute_input": "2024-09-26T14:51:35.179788Z", + "iopub.status.busy": "2024-09-26T14:51:35.179361Z", + "iopub.status.idle": "2024-09-26T14:51:37.765581Z", + "shell.execute_reply": "2024-09-26T14:51:37.764993Z" } }, "outputs": [ @@ -235,7 +235,7 @@ "Finding class_imbalance issues ...\n", "Finding underperforming_group issues ...\n", "\n", - "Audit complete. 523 issues found in the dataset.\n" + "Audit complete. 524 issues found in the dataset.\n" ] }, { @@ -280,13 +280,13 @@ " \n", " 2\n", " outlier\n", - " 0.356958\n", - " 362\n", + " 0.356924\n", + " 363\n", " \n", " \n", " 3\n", " near_duplicate\n", - " 0.619565\n", + " 0.619581\n", " 108\n", " \n", " \n", @@ -315,8 +315,8 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356958 362\n", - "3 near_duplicate 0.619565 108\n", + "2 outlier 0.356924 363\n", + "3 near_duplicate 0.619581 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", "6 underperforming_group 0.651838 0" @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:33.000426Z", - "iopub.status.busy": "2024-09-06T19:37:32.999838Z", - "iopub.status.idle": "2024-09-06T19:37:42.839981Z", - "shell.execute_reply": "2024-09-06T19:37:42.839475Z" + "iopub.execute_input": "2024-09-26T14:51:37.767993Z", + "iopub.status.busy": "2024-09-26T14:51:37.767425Z", + "iopub.status.idle": "2024-09-26T14:51:46.526023Z", + "shell.execute_reply": "2024-09-26T14:51:46.525421Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:42.842458Z", - "iopub.status.busy": "2024-09-06T19:37:42.842052Z", - "iopub.status.idle": "2024-09-06T19:37:43.014469Z", - "shell.execute_reply": "2024-09-06T19:37:43.013871Z" + "iopub.execute_input": "2024-09-26T14:51:46.528043Z", + "iopub.status.busy": "2024-09-26T14:51:46.527681Z", + "iopub.status.idle": "2024-09-26T14:51:46.730683Z", + "shell.execute_reply": "2024-09-26T14:51:46.730045Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:43.016817Z", - "iopub.status.busy": "2024-09-06T19:37:43.016641Z", - "iopub.status.idle": "2024-09-06T19:37:44.396004Z", - "shell.execute_reply": "2024-09-06T19:37:44.395431Z" + "iopub.execute_input": "2024-09-26T14:51:46.732793Z", + "iopub.status.busy": "2024-09-26T14:51:46.732448Z", + "iopub.status.idle": "2024-09-26T14:51:48.255623Z", + "shell.execute_reply": "2024-09-26T14:51:48.255118Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.398298Z", - "iopub.status.busy": "2024-09-06T19:37:44.397931Z", - "iopub.status.idle": "2024-09-06T19:37:44.810929Z", - "shell.execute_reply": "2024-09-06T19:37:44.810371Z" + "iopub.execute_input": "2024-09-26T14:51:48.257484Z", + "iopub.status.busy": "2024-09-26T14:51:48.257119Z", + "iopub.status.idle": "2024-09-26T14:51:48.773736Z", + "shell.execute_reply": "2024-09-26T14:51:48.773135Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.813440Z", - "iopub.status.busy": "2024-09-06T19:37:44.812940Z", - "iopub.status.idle": "2024-09-06T19:37:44.826271Z", - "shell.execute_reply": "2024-09-06T19:37:44.825842Z" + "iopub.execute_input": "2024-09-26T14:51:48.775864Z", + "iopub.status.busy": "2024-09-26T14:51:48.775323Z", + "iopub.status.idle": "2024-09-26T14:51:48.790103Z", + "shell.execute_reply": "2024-09-26T14:51:48.789626Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.828390Z", - "iopub.status.busy": "2024-09-06T19:37:44.828044Z", - "iopub.status.idle": "2024-09-06T19:37:44.847179Z", - "shell.execute_reply": "2024-09-06T19:37:44.846760Z" + "iopub.execute_input": "2024-09-26T14:51:48.791833Z", + "iopub.status.busy": "2024-09-26T14:51:48.791503Z", + "iopub.status.idle": "2024-09-26T14:51:48.810723Z", + "shell.execute_reply": "2024-09-26T14:51:48.810137Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:44.849314Z", - "iopub.status.busy": "2024-09-06T19:37:44.848979Z", - "iopub.status.idle": "2024-09-06T19:37:45.077019Z", - "shell.execute_reply": "2024-09-06T19:37:45.076447Z" + "iopub.execute_input": "2024-09-26T14:51:48.812726Z", + "iopub.status.busy": "2024-09-26T14:51:48.812340Z", + "iopub.status.idle": "2024-09-26T14:51:49.055015Z", + "shell.execute_reply": "2024-09-26T14:51:49.054398Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.079688Z", - "iopub.status.busy": "2024-09-06T19:37:45.079281Z", - "iopub.status.idle": "2024-09-06T19:37:45.098946Z", - "shell.execute_reply": "2024-09-06T19:37:45.098466Z" + "iopub.execute_input": "2024-09-26T14:51:49.057480Z", + "iopub.status.busy": "2024-09-26T14:51:49.057052Z", + "iopub.status.idle": "2024-09-26T14:51:49.076673Z", + "shell.execute_reply": "2024-09-26T14:51:49.076195Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.101100Z", - "iopub.status.busy": "2024-09-06T19:37:45.100762Z", - "iopub.status.idle": "2024-09-06T19:37:45.277489Z", - "shell.execute_reply": "2024-09-06T19:37:45.276850Z" + "iopub.execute_input": "2024-09-26T14:51:49.078495Z", + "iopub.status.busy": "2024-09-26T14:51:49.078146Z", + "iopub.status.idle": "2024-09-26T14:51:49.248180Z", + "shell.execute_reply": "2024-09-26T14:51:49.247594Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.279928Z", - "iopub.status.busy": "2024-09-06T19:37:45.279722Z", - "iopub.status.idle": "2024-09-06T19:37:45.290798Z", - "shell.execute_reply": "2024-09-06T19:37:45.290229Z" + "iopub.execute_input": "2024-09-26T14:51:49.250291Z", + "iopub.status.busy": "2024-09-26T14:51:49.249923Z", + "iopub.status.idle": "2024-09-26T14:51:49.260161Z", + "shell.execute_reply": "2024-09-26T14:51:49.259683Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.292867Z", - "iopub.status.busy": "2024-09-06T19:37:45.292672Z", - "iopub.status.idle": "2024-09-06T19:37:45.302178Z", - "shell.execute_reply": "2024-09-06T19:37:45.301745Z" + "iopub.execute_input": "2024-09-26T14:51:49.261950Z", + "iopub.status.busy": "2024-09-26T14:51:49.261604Z", + "iopub.status.idle": "2024-09-26T14:51:49.271258Z", + "shell.execute_reply": "2024-09-26T14:51:49.270689Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.304034Z", - "iopub.status.busy": "2024-09-06T19:37:45.303861Z", - "iopub.status.idle": "2024-09-06T19:37:45.329485Z", - "shell.execute_reply": "2024-09-06T19:37:45.329066Z" + "iopub.execute_input": "2024-09-26T14:51:49.272963Z", + "iopub.status.busy": "2024-09-26T14:51:49.272785Z", + "iopub.status.idle": "2024-09-26T14:51:49.300283Z", + "shell.execute_reply": "2024-09-26T14:51:49.299657Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.331450Z", - "iopub.status.busy": "2024-09-06T19:37:45.331118Z", - "iopub.status.idle": "2024-09-06T19:37:45.333941Z", - "shell.execute_reply": "2024-09-06T19:37:45.333348Z" + "iopub.execute_input": "2024-09-26T14:51:49.302435Z", + "iopub.status.busy": "2024-09-26T14:51:49.302020Z", + "iopub.status.idle": "2024-09-26T14:51:49.304853Z", + "shell.execute_reply": "2024-09-26T14:51:49.304388Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.336081Z", - "iopub.status.busy": "2024-09-06T19:37:45.335742Z", - "iopub.status.idle": "2024-09-06T19:37:45.354797Z", - "shell.execute_reply": "2024-09-06T19:37:45.354315Z" + "iopub.execute_input": "2024-09-26T14:51:49.306559Z", + "iopub.status.busy": "2024-09-26T14:51:49.306373Z", + "iopub.status.idle": "2024-09-26T14:51:49.326211Z", + "shell.execute_reply": "2024-09-26T14:51:49.325620Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.356897Z", - "iopub.status.busy": "2024-09-06T19:37:45.356543Z", - "iopub.status.idle": "2024-09-06T19:37:45.360935Z", - "shell.execute_reply": "2024-09-06T19:37:45.360328Z" + "iopub.execute_input": "2024-09-26T14:51:49.328491Z", + "iopub.status.busy": "2024-09-26T14:51:49.327912Z", + "iopub.status.idle": "2024-09-26T14:51:49.332250Z", + "shell.execute_reply": "2024-09-26T14:51:49.331798Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.363152Z", - "iopub.status.busy": "2024-09-06T19:37:45.362835Z", - "iopub.status.idle": "2024-09-06T19:37:45.390311Z", - "shell.execute_reply": "2024-09-06T19:37:45.389739Z" + "iopub.execute_input": "2024-09-26T14:51:49.334080Z", + "iopub.status.busy": "2024-09-26T14:51:49.333676Z", + "iopub.status.idle": "2024-09-26T14:51:49.363534Z", + "shell.execute_reply": "2024-09-26T14:51:49.362928Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.392321Z", - "iopub.status.busy": "2024-09-06T19:37:45.392005Z", - "iopub.status.idle": "2024-09-06T19:37:45.759141Z", - "shell.execute_reply": "2024-09-06T19:37:45.758581Z" + "iopub.execute_input": "2024-09-26T14:51:49.365331Z", + "iopub.status.busy": "2024-09-26T14:51:49.365032Z", + "iopub.status.idle": "2024-09-26T14:51:49.727339Z", + "shell.execute_reply": "2024-09-26T14:51:49.726743Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.761452Z", - "iopub.status.busy": "2024-09-06T19:37:45.761084Z", - "iopub.status.idle": "2024-09-06T19:37:45.764398Z", - "shell.execute_reply": "2024-09-06T19:37:45.763923Z" + "iopub.execute_input": "2024-09-26T14:51:49.729270Z", + "iopub.status.busy": "2024-09-26T14:51:49.729071Z", + "iopub.status.idle": "2024-09-26T14:51:49.732072Z", + "shell.execute_reply": "2024-09-26T14:51:49.731620Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.766685Z", - "iopub.status.busy": "2024-09-06T19:37:45.766351Z", - "iopub.status.idle": "2024-09-06T19:37:45.779490Z", - "shell.execute_reply": "2024-09-06T19:37:45.779045Z" + "iopub.execute_input": "2024-09-26T14:51:49.733810Z", + "iopub.status.busy": "2024-09-26T14:51:49.733632Z", + "iopub.status.idle": "2024-09-26T14:51:49.747657Z", + "shell.execute_reply": "2024-09-26T14:51:49.747198Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.781428Z", - "iopub.status.busy": "2024-09-06T19:37:45.781250Z", - "iopub.status.idle": "2024-09-06T19:37:45.796041Z", - "shell.execute_reply": "2024-09-06T19:37:45.795601Z" + "iopub.execute_input": "2024-09-26T14:51:49.749243Z", + "iopub.status.busy": "2024-09-26T14:51:49.749065Z", + "iopub.status.idle": "2024-09-26T14:51:49.763193Z", + "shell.execute_reply": "2024-09-26T14:51:49.762714Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.798043Z", - "iopub.status.busy": "2024-09-06T19:37:45.797870Z", - "iopub.status.idle": "2024-09-06T19:37:45.807740Z", - "shell.execute_reply": "2024-09-06T19:37:45.807165Z" + "iopub.execute_input": "2024-09-26T14:51:49.764801Z", + "iopub.status.busy": "2024-09-26T14:51:49.764624Z", + "iopub.status.idle": "2024-09-26T14:51:49.775091Z", + "shell.execute_reply": "2024-09-26T14:51:49.774491Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.809952Z", - "iopub.status.busy": "2024-09-06T19:37:45.809629Z", - "iopub.status.idle": "2024-09-06T19:37:45.818832Z", - "shell.execute_reply": "2024-09-06T19:37:45.818256Z" + "iopub.execute_input": "2024-09-26T14:51:49.777122Z", + "iopub.status.busy": "2024-09-26T14:51:49.776798Z", + "iopub.status.idle": "2024-09-26T14:51:49.786610Z", + "shell.execute_reply": "2024-09-26T14:51:49.786151Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.821154Z", - "iopub.status.busy": "2024-09-06T19:37:45.820691Z", - "iopub.status.idle": "2024-09-06T19:37:45.824900Z", - "shell.execute_reply": "2024-09-06T19:37:45.824317Z" + "iopub.execute_input": "2024-09-26T14:51:49.788278Z", + "iopub.status.busy": "2024-09-26T14:51:49.788101Z", + "iopub.status.idle": "2024-09-26T14:51:49.791818Z", + "shell.execute_reply": "2024-09-26T14:51:49.791364Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.826963Z", - "iopub.status.busy": "2024-09-06T19:37:45.826647Z", - "iopub.status.idle": "2024-09-06T19:37:45.876648Z", - "shell.execute_reply": "2024-09-06T19:37:45.876084Z" + "iopub.execute_input": "2024-09-26T14:51:49.793563Z", + "iopub.status.busy": "2024-09-26T14:51:49.793225Z", + "iopub.status.idle": "2024-09-26T14:51:49.849225Z", + "shell.execute_reply": "2024-09-26T14:51:49.848755Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
8nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
1nanFemaleRural6421.1600005.000000NaTFalse0.666667
9nanMaleRural4655.8200001.000000NaTFalse0.666667
14nanMaleRural6790.4600003.000000NaTFalse0.666667
13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.878907Z", - "iopub.status.busy": "2024-09-06T19:37:45.878480Z", - "iopub.status.idle": "2024-09-06T19:37:45.884204Z", - "shell.execute_reply": "2024-09-06T19:37:45.883634Z" + "iopub.execute_input": "2024-09-26T14:51:49.851334Z", + "iopub.status.busy": "2024-09-26T14:51:49.850848Z", + "iopub.status.idle": "2024-09-26T14:51:49.856692Z", + "shell.execute_reply": "2024-09-26T14:51:49.856243Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.886291Z", - "iopub.status.busy": "2024-09-06T19:37:45.885973Z", - "iopub.status.idle": "2024-09-06T19:37:45.897008Z", - "shell.execute_reply": "2024-09-06T19:37:45.896438Z" + "iopub.execute_input": "2024-09-26T14:51:49.858413Z", + "iopub.status.busy": "2024-09-26T14:51:49.858094Z", + "iopub.status.idle": "2024-09-26T14:51:49.869805Z", + "shell.execute_reply": "2024-09-26T14:51:49.869218Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:45.899243Z", - "iopub.status.busy": "2024-09-06T19:37:45.898904Z", - "iopub.status.idle": "2024-09-06T19:37:46.075809Z", - "shell.execute_reply": "2024-09-06T19:37:46.075226Z" + "iopub.execute_input": "2024-09-26T14:51:49.871476Z", + "iopub.status.busy": "2024-09-26T14:51:49.871161Z", + "iopub.status.idle": "2024-09-26T14:51:50.098032Z", + "shell.execute_reply": "2024-09-26T14:51:50.097456Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.078430Z", - "iopub.status.busy": "2024-09-06T19:37:46.077957Z", - "iopub.status.idle": "2024-09-06T19:37:46.085812Z", - "shell.execute_reply": "2024-09-06T19:37:46.085244Z" + "iopub.execute_input": "2024-09-26T14:51:50.099892Z", + "iopub.status.busy": "2024-09-26T14:51:50.099599Z", + "iopub.status.idle": "2024-09-26T14:51:50.107584Z", + "shell.execute_reply": "2024-09-26T14:51:50.107015Z" }, "nbsphinx": "hidden" }, @@ -3756,10 +3756,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.087762Z", - "iopub.status.busy": "2024-09-06T19:37:46.087589Z", - "iopub.status.idle": "2024-09-06T19:37:46.522443Z", - "shell.execute_reply": "2024-09-06T19:37:46.521749Z" + "iopub.execute_input": "2024-09-26T14:51:50.109288Z", + "iopub.status.busy": "2024-09-26T14:51:50.109111Z", + "iopub.status.idle": "2024-09-26T14:51:50.496608Z", + "shell.execute_reply": "2024-09-26T14:51:50.495787Z" } }, "outputs": [ @@ -3767,7 +3767,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:37:46-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", + "--2024-09-26 14:51:50-- https://s.cleanlab.ai/CIFAR-10-subset.zip\r\n", "Resolving s.cleanlab.ai (s.cleanlab.ai)... 185.199.111.153, 185.199.110.153, 185.199.109.153, ...\r\n", "Connecting to s.cleanlab.ai (s.cleanlab.ai)|185.199.111.153|:443... connected.\r\n", "HTTP request sent, awaiting response... " @@ -3783,9 +3783,9 @@ "\r\n", "\r", "CIFAR-10-subset.zip 0%[ ] 0 --.-KB/s \r", - "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.005s \r\n", + "CIFAR-10-subset.zip 100%[===================>] 963.58K --.-KB/s in 0.009s \r\n", "\r\n", - "2024-09-06 19:37:46 (176 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", + "2024-09-26 14:51:50 (107 MB/s) - ‘CIFAR-10-subset.zip’ saved [986707/986707]\r\n", "\r\n" ] } @@ -3801,10 +3801,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:46.525178Z", - "iopub.status.busy": "2024-09-06T19:37:46.524748Z", - "iopub.status.idle": "2024-09-06T19:37:48.452276Z", - "shell.execute_reply": "2024-09-06T19:37:48.451758Z" + "iopub.execute_input": "2024-09-26T14:51:50.499275Z", + "iopub.status.busy": "2024-09-26T14:51:50.498755Z", + "iopub.status.idle": "2024-09-26T14:51:52.468119Z", + "shell.execute_reply": "2024-09-26T14:51:52.467505Z" } }, "outputs": [], @@ -3850,10 +3850,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:48.454913Z", - "iopub.status.busy": "2024-09-06T19:37:48.454468Z", - "iopub.status.idle": "2024-09-06T19:37:49.092778Z", - "shell.execute_reply": "2024-09-06T19:37:49.092169Z" + "iopub.execute_input": "2024-09-26T14:51:52.470295Z", + "iopub.status.busy": "2024-09-26T14:51:52.470006Z", + "iopub.status.idle": "2024-09-26T14:51:53.135612Z", + "shell.execute_reply": "2024-09-26T14:51:53.134933Z" } }, "outputs": [ @@ -3868,7 +3868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a5793cf283c046f188f735beef4577a5", + "model_id": "819cd513a50348b98c0ff3c8dd72c7bd", "version_major": 2, "version_minor": 0 }, @@ -4008,10 +4008,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.095580Z", - "iopub.status.busy": "2024-09-06T19:37:49.095115Z", - "iopub.status.idle": "2024-09-06T19:37:49.108940Z", - "shell.execute_reply": "2024-09-06T19:37:49.108334Z" + "iopub.execute_input": "2024-09-26T14:51:53.138593Z", + "iopub.status.busy": "2024-09-26T14:51:53.138086Z", + "iopub.status.idle": "2024-09-26T14:51:53.152674Z", + "shell.execute_reply": "2024-09-26T14:51:53.152106Z" } }, "outputs": [ @@ -4257,10 +4257,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.112413Z", - "iopub.status.busy": "2024-09-06T19:37:49.112212Z", - "iopub.status.idle": "2024-09-06T19:37:49.262201Z", - "shell.execute_reply": "2024-09-06T19:37:49.261645Z" + "iopub.execute_input": "2024-09-26T14:51:53.155019Z", + "iopub.status.busy": "2024-09-26T14:51:53.154607Z", + "iopub.status.idle": "2024-09-26T14:51:53.305855Z", + "shell.execute_reply": "2024-09-26T14:51:53.305327Z" } }, "outputs": [ @@ -4325,10 +4325,10 @@ "execution_count": 38, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.264493Z", - "iopub.status.busy": "2024-09-06T19:37:49.264138Z", - "iopub.status.idle": "2024-09-06T19:37:49.776468Z", - "shell.execute_reply": "2024-09-06T19:37:49.775810Z" + "iopub.execute_input": "2024-09-26T14:51:53.308217Z", + "iopub.status.busy": "2024-09-26T14:51:53.307686Z", + "iopub.status.idle": "2024-09-26T14:51:53.823497Z", + "shell.execute_reply": "2024-09-26T14:51:53.822950Z" }, "nbsphinx": "hidden" }, @@ -4344,7 +4344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e53b81d02870488ca1d70faf1534371f", + "model_id": "ac8a16cb60b04919938bc00b2f1342f7", "version_major": 2, "version_minor": 0 }, @@ -4598,10 +4598,10 @@ "execution_count": 39, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:49.778901Z", - "iopub.status.busy": "2024-09-06T19:37:49.778528Z", - "iopub.status.idle": "2024-09-06T19:37:49.924980Z", - "shell.execute_reply": "2024-09-06T19:37:49.924477Z" + "iopub.execute_input": "2024-09-26T14:51:53.825382Z", + "iopub.status.busy": "2024-09-26T14:51:53.825164Z", + "iopub.status.idle": "2024-09-26T14:51:53.978845Z", + "shell.execute_reply": "2024-09-26T14:51:53.978305Z" }, "nbsphinx": "hidden" }, @@ -4648,12 +4648,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "021a50164b8c491ebb069bd57b11ce1a": { + "0cd32d52503a444d88252596c7202d70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4706,30 +4706,7 @@ "width": null } }, - "2cb88e5e7d0f4849b336950480e87a06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5349f02a0bb24786bba46192aa1d90ff", - "placeholder": "​", - "style": "IPY_MODEL_b93c4b8b97f34f0b93a2d334e5065e1b", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "313b234230ce4ce4850b3fa6a5e1b1ee": { + "1e281a0c15b84c80941bc82a97097993": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4782,7 +4759,7 @@ "width": null } }, - "41bdd318b6d1453a8daca74a0776e419": { + "2680a7b149fd4520bb536ef2dfbaa7c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4835,7 +4812,7 @@ "width": null } }, - "5349f02a0bb24786bba46192aa1d90ff": { + "2ea4ee3035ec4460b45119db4b86c88e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4888,105 +4865,25 @@ "width": null } }, - "5664879b48124f5cac1e0a8c43742995": { - "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_da820a1ccd2b42d4a8c12ea0328d1169", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_da8ad4a548a8409389fab7ddc0e601bc", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "6529bc3e5e35424f967dab0385030a5c": { + "305f6e33eec84265b93317eacfe9b6b0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_41bdd318b6d1453a8daca74a0776e419", - "placeholder": "​", - "style": "IPY_MODEL_dfac24cbd04d4a6a9c6a2f3d7e34c87e", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 682.83it/s]" - } - }, - "733b0d114c6e48e6af9ced8acfb5bf3a": { - "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_953f4c82aabd472c9e8dfebdf70939d8", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_86445ca79c764836a406520c67b4b945", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "7b9c39c715b849dbb886ceaeb96e5c35": { - "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_7fb3eb018b9d446294207573ca64cda2", - "placeholder": "​", - "style": "IPY_MODEL_ec3f09ac595d4dadbd0cf34793d57087", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "7fb3eb018b9d446294207573ca64cda2": { + "31a345d79d134ece901060efdb94b165": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5039,23 +4936,7 @@ "width": null } }, - "86445ca79c764836a406520c67b4b945": { - "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": "" - } - }, - "953f4c82aabd472c9e8dfebdf70939d8": { + "31b855e014d84579b8434de0e51b2846": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5108,7 +4989,97 @@ "width": null } }, - "9ffc7a8014b64edfad1dd643172601d1": { + "4a9cf0b92b274885a650766f05b77292": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31b855e014d84579b8434de0e51b2846", + "placeholder": "​", + "style": "IPY_MODEL_77f44dc1b8c84ab4aef3e983303eb4a2", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 696.05it/s]" + } + }, + "56f377970b0e41d0a3ab38bfadd0c51b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2680a7b149fd4520bb536ef2dfbaa7c2", + "placeholder": "​", + "style": "IPY_MODEL_a9661f0b7a8d447c8c47dfe0b78f61ef", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "59cfff3ec22848b6944ba0bf323bd24b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5f01f9c656284899b3a91282330101c8": { + "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_829ff0aad29b4270b40cd9499bc93cc7", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_77633068b2ef4937bf213a3297280a11", + "tabbable": null, + "tooltip": null, + "value": 200.0 + } + }, + "769fa00f56a8417b92d2a48b7c419f62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5123,15 +5094,73 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_021a50164b8c491ebb069bd57b11ce1a", + "layout": "IPY_MODEL_31a345d79d134ece901060efdb94b165", "placeholder": "​", - "style": "IPY_MODEL_da74a2af2dfa4378a23a6009ae2f264c", + "style": "IPY_MODEL_305f6e33eec84265b93317eacfe9b6b0", "tabbable": null, "tooltip": null, - "value": " 200/200 [00:00<00:00, 785.38it/s]" + "value": " 200/200 [00:00<00:00, 735.07it/s]" + } + }, + "77633068b2ef4937bf213a3297280a11": { + "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": "" + } + }, + "77f44dc1b8c84ab4aef3e983303eb4a2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "a185cb088b4a4b50933699f586275482": { + "819cd513a50348b98c0ff3c8dd72c7bd": { + "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_92ac731fb51746b9a27792595b65bd81", + "IPY_MODEL_e345abf69b7b4519a731b4da99441fe1", + "IPY_MODEL_769fa00f56a8417b92d2a48b7c419f62" + ], + "layout": "IPY_MODEL_2ea4ee3035ec4460b45119db4b86c88e", + "tabbable": null, + "tooltip": null + } + }, + "829ff0aad29b4270b40cd9499bc93cc7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5184,31 +5213,30 @@ "width": null } }, - "a5793cf283c046f188f735beef4577a5": { + "92ac731fb51746b9a27792595b65bd81": { "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_7b9c39c715b849dbb886ceaeb96e5c35", - "IPY_MODEL_5664879b48124f5cac1e0a8c43742995", - "IPY_MODEL_9ffc7a8014b64edfad1dd643172601d1" - ], - "layout": "IPY_MODEL_313b234230ce4ce4850b3fa6a5e1b1ee", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cd32d52503a444d88252596c7202d70", + "placeholder": "​", + "style": "IPY_MODEL_59cfff3ec22848b6944ba0bf323bd24b", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "b93c4b8b97f34f0b93a2d334e5065e1b": { + "a9661f0b7a8d447c8c47dfe0b78f61ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5226,25 +5254,31 @@ "text_color": null } }, - "da74a2af2dfa4378a23a6009ae2f264c": { + "ac8a16cb60b04919938bc00b2f1342f7": { "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_56f377970b0e41d0a3ab38bfadd0c51b", + "IPY_MODEL_5f01f9c656284899b3a91282330101c8", + "IPY_MODEL_4a9cf0b92b274885a650766f05b77292" + ], + "layout": "IPY_MODEL_b805213db2034e85bcf8aa102ab8cd3c", + "tabbable": null, + "tooltip": null } }, - "da820a1ccd2b42d4a8c12ea0328d1169": { + "b805213db2034e85bcf8aa102ab8cd3c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5297,7 +5331,7 @@ "width": null } }, - "da8ad4a548a8409389fab7ddc0e601bc": { + "c28c38c2f92e4620b4be9878c38511ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5313,64 +5347,30 @@ "description_width": "" } }, - "dfac24cbd04d4a6a9c6a2f3d7e34c87e": { + "e345abf69b7b4519a731b4da99441fe1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "e53b81d02870488ca1d70faf1534371f": { - "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_2cb88e5e7d0f4849b336950480e87a06", - "IPY_MODEL_733b0d114c6e48e6af9ced8acfb5bf3a", - "IPY_MODEL_6529bc3e5e35424f967dab0385030a5c" - ], - "layout": "IPY_MODEL_a185cb088b4a4b50933699f586275482", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1e281a0c15b84c80941bc82a97097993", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c28c38c2f92e4620b4be9878c38511ce", "tabbable": null, - "tooltip": null - } - }, - "ec3f09ac595d4dadbd0cf34793d57087": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": 200.0 } } }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index e932968f7..c8c374ab4 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": "2024-09-06T19:37:53.970574Z", - "iopub.status.busy": "2024-09-06T19:37:53.970388Z", - "iopub.status.idle": "2024-09-06T19:37:55.134808Z", - "shell.execute_reply": "2024-09-06T19:37:55.134157Z" + "iopub.execute_input": "2024-09-26T14:51:59.182546Z", + "iopub.status.busy": "2024-09-26T14:51:59.182366Z", + "iopub.status.idle": "2024-09-26T14:52:00.393643Z", + "shell.execute_reply": "2024-09-26T14:52:00.393076Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:37:55.137505Z", - "iopub.status.busy": "2024-09-06T19:37:55.137230Z", - "iopub.status.idle": "2024-09-06T19:37:55.140659Z", - "shell.execute_reply": "2024-09-06T19:37:55.140221Z" + "iopub.execute_input": "2024-09-26T14:52:00.395685Z", + "iopub.status.busy": "2024-09-26T14:52:00.395388Z", + "iopub.status.idle": "2024-09-26T14:52:00.398322Z", + "shell.execute_reply": "2024-09-26T14:52:00.397857Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:55.142857Z", - "iopub.status.busy": "2024-09-06T19:37:55.142554Z", - "iopub.status.idle": "2024-09-06T19:37:55.154394Z", - "shell.execute_reply": "2024-09-06T19:37:55.153913Z" + "iopub.execute_input": "2024-09-26T14:52:00.400144Z", + "iopub.status.busy": "2024-09-26T14:52:00.399840Z", + "iopub.status.idle": "2024-09-26T14:52:00.412193Z", + "shell.execute_reply": "2024-09-26T14:52:00.411697Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:37:55.156367Z", - "iopub.status.busy": "2024-09-06T19:37:55.156193Z", - "iopub.status.idle": "2024-09-06T19:38:03.213180Z", - "shell.execute_reply": "2024-09-06T19:38:03.212490Z" + "iopub.execute_input": "2024-09-26T14:52:00.414113Z", + "iopub.status.busy": "2024-09-26T14:52:00.413741Z", + "iopub.status.idle": "2024-09-26T14:52:05.730687Z", + "shell.execute_reply": "2024-09-26T14:52:05.730191Z" }, "id": "dhTHOg8Pyv5G" }, @@ -3119,7 +3119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index 5da9f6de6..13b6a03e5 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -835,13 +835,13 @@

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

-
+
-
+
@@ -1706,7 +1706,7 @@

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 cec52a458..6f20431e7 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:05.442254Z", - "iopub.status.busy": "2024-09-06T19:38:05.441754Z", - "iopub.status.idle": "2024-09-06T19:38:06.608058Z", - "shell.execute_reply": "2024-09-06T19:38:06.607439Z" + "iopub.execute_input": "2024-09-26T14:52:08.034662Z", + "iopub.status.busy": "2024-09-26T14:52:08.034481Z", + "iopub.status.idle": "2024-09-26T14:52:09.304690Z", + "shell.execute_reply": "2024-09-26T14:52:09.304102Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:06.610846Z", - "iopub.status.busy": "2024-09-06T19:38:06.610375Z", - "iopub.status.idle": "2024-09-06T19:38:06.613802Z", - "shell.execute_reply": "2024-09-06T19:38:06.613322Z" + "iopub.execute_input": "2024-09-26T14:52:09.306879Z", + "iopub.status.busy": "2024-09-26T14:52:09.306585Z", + "iopub.status.idle": "2024-09-26T14:52:09.310196Z", + "shell.execute_reply": "2024-09-26T14:52:09.309631Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:06.615798Z", - "iopub.status.busy": "2024-09-06T19:38:06.615518Z", - "iopub.status.idle": "2024-09-06T19:38:09.981363Z", - "shell.execute_reply": "2024-09-06T19:38:09.980664Z" + "iopub.execute_input": "2024-09-26T14:52:09.311928Z", + "iopub.status.busy": "2024-09-26T14:52:09.311543Z", + "iopub.status.idle": "2024-09-26T14:52:12.757719Z", + "shell.execute_reply": "2024-09-26T14:52:12.756901Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:09.984620Z", - "iopub.status.busy": "2024-09-06T19:38:09.983724Z", - "iopub.status.idle": "2024-09-06T19:38:10.027299Z", - "shell.execute_reply": "2024-09-06T19:38:10.026694Z" + "iopub.execute_input": "2024-09-26T14:52:12.760355Z", + "iopub.status.busy": "2024-09-26T14:52:12.759696Z", + "iopub.status.idle": "2024-09-26T14:52:12.813184Z", + "shell.execute_reply": "2024-09-26T14:52:12.812421Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.030074Z", - "iopub.status.busy": "2024-09-06T19:38:10.029673Z", - "iopub.status.idle": "2024-09-06T19:38:10.069413Z", - "shell.execute_reply": "2024-09-06T19:38:10.068633Z" + "iopub.execute_input": "2024-09-26T14:52:12.815571Z", + "iopub.status.busy": "2024-09-26T14:52:12.815173Z", + "iopub.status.idle": "2024-09-26T14:52:12.861989Z", + "shell.execute_reply": "2024-09-26T14:52:12.861319Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.072131Z", - "iopub.status.busy": "2024-09-06T19:38:10.071875Z", - "iopub.status.idle": "2024-09-06T19:38:10.075127Z", - "shell.execute_reply": "2024-09-06T19:38:10.074582Z" + "iopub.execute_input": "2024-09-26T14:52:12.864397Z", + "iopub.status.busy": "2024-09-26T14:52:12.863906Z", + "iopub.status.idle": "2024-09-26T14:52:12.867232Z", + "shell.execute_reply": "2024-09-26T14:52:12.866761Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.077352Z", - "iopub.status.busy": "2024-09-06T19:38:10.077011Z", - "iopub.status.idle": "2024-09-06T19:38:10.079576Z", - "shell.execute_reply": "2024-09-06T19:38:10.079132Z" + "iopub.execute_input": "2024-09-26T14:52:12.868891Z", + "iopub.status.busy": "2024-09-26T14:52:12.868591Z", + "iopub.status.idle": "2024-09-26T14:52:12.871312Z", + "shell.execute_reply": "2024-09-26T14:52:12.870766Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.081910Z", - "iopub.status.busy": "2024-09-06T19:38:10.081719Z", - "iopub.status.idle": "2024-09-06T19:38:10.109741Z", - "shell.execute_reply": "2024-09-06T19:38:10.109183Z" + "iopub.execute_input": "2024-09-26T14:52:12.873230Z", + "iopub.status.busy": "2024-09-26T14:52:12.872884Z", + "iopub.status.idle": "2024-09-26T14:52:12.897801Z", + "shell.execute_reply": "2024-09-26T14:52:12.897165Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10e11ec38b13425280381ff5281c4450", + "model_id": "554f0bffd2414657b0244763906a1e3d", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e2d5adb59434e2081db18c696100263", + "model_id": "d70c6118368a40e3b8c24ac57cc4db26", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.115104Z", - "iopub.status.busy": "2024-09-06T19:38:10.114762Z", - "iopub.status.idle": "2024-09-06T19:38:10.121297Z", - "shell.execute_reply": "2024-09-06T19:38:10.120726Z" + "iopub.execute_input": "2024-09-26T14:52:12.900530Z", + "iopub.status.busy": "2024-09-26T14:52:12.900181Z", + "iopub.status.idle": "2024-09-26T14:52:12.907197Z", + "shell.execute_reply": "2024-09-26T14:52:12.906763Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.123497Z", - "iopub.status.busy": "2024-09-06T19:38:10.123043Z", - "iopub.status.idle": "2024-09-06T19:38:10.126503Z", - "shell.execute_reply": "2024-09-06T19:38:10.126056Z" + "iopub.execute_input": "2024-09-26T14:52:12.908993Z", + "iopub.status.busy": "2024-09-26T14:52:12.908664Z", + "iopub.status.idle": "2024-09-26T14:52:12.911903Z", + "shell.execute_reply": "2024-09-26T14:52:12.911461Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.128505Z", - "iopub.status.busy": "2024-09-06T19:38:10.128204Z", - "iopub.status.idle": "2024-09-06T19:38:10.134549Z", - "shell.execute_reply": "2024-09-06T19:38:10.134003Z" + "iopub.execute_input": "2024-09-26T14:52:12.913714Z", + "iopub.status.busy": "2024-09-26T14:52:12.913385Z", + "iopub.status.idle": "2024-09-26T14:52:12.919520Z", + "shell.execute_reply": "2024-09-26T14:52:12.919085Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.136656Z", - "iopub.status.busy": "2024-09-06T19:38:10.136338Z", - "iopub.status.idle": "2024-09-06T19:38:10.179181Z", - "shell.execute_reply": "2024-09-06T19:38:10.178556Z" + "iopub.execute_input": "2024-09-26T14:52:12.921164Z", + "iopub.status.busy": "2024-09-26T14:52:12.920839Z", + "iopub.status.idle": "2024-09-26T14:52:12.968393Z", + "shell.execute_reply": "2024-09-26T14:52:12.967757Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.181945Z", - "iopub.status.busy": "2024-09-06T19:38:10.181555Z", - "iopub.status.idle": "2024-09-06T19:38:10.218200Z", - "shell.execute_reply": "2024-09-06T19:38:10.217453Z" + "iopub.execute_input": "2024-09-26T14:52:12.970571Z", + "iopub.status.busy": "2024-09-26T14:52:12.970308Z", + "iopub.status.idle": "2024-09-26T14:52:13.022776Z", + "shell.execute_reply": "2024-09-26T14:52:13.022011Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.220958Z", - "iopub.status.busy": "2024-09-06T19:38:10.220569Z", - "iopub.status.idle": "2024-09-06T19:38:10.349381Z", - "shell.execute_reply": "2024-09-06T19:38:10.348725Z" + "iopub.execute_input": "2024-09-26T14:52:13.025203Z", + "iopub.status.busy": "2024-09-26T14:52:13.024937Z", + "iopub.status.idle": "2024-09-26T14:52:13.170260Z", + "shell.execute_reply": "2024-09-26T14:52:13.169652Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:10.352202Z", - "iopub.status.busy": "2024-09-06T19:38:10.351437Z", - "iopub.status.idle": "2024-09-06T19:38:13.390257Z", - "shell.execute_reply": "2024-09-06T19:38:13.389586Z" + "iopub.execute_input": "2024-09-26T14:52:13.172750Z", + "iopub.status.busy": "2024-09-26T14:52:13.171949Z", + "iopub.status.idle": "2024-09-26T14:52:16.250921Z", + "shell.execute_reply": "2024-09-26T14:52:16.250318Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.392707Z", - "iopub.status.busy": "2024-09-06T19:38:13.392511Z", - "iopub.status.idle": "2024-09-06T19:38:13.450827Z", - "shell.execute_reply": "2024-09-06T19:38:13.450261Z" + "iopub.execute_input": "2024-09-26T14:52:16.253054Z", + "iopub.status.busy": "2024-09-26T14:52:16.252685Z", + "iopub.status.idle": "2024-09-26T14:52:16.313315Z", + "shell.execute_reply": "2024-09-26T14:52:16.312808Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.453108Z", - "iopub.status.busy": "2024-09-06T19:38:13.452688Z", - "iopub.status.idle": "2024-09-06T19:38:13.493414Z", - "shell.execute_reply": "2024-09-06T19:38:13.492941Z" + "iopub.execute_input": "2024-09-26T14:52:16.315165Z", + "iopub.status.busy": "2024-09-26T14:52:16.314831Z", + "iopub.status.idle": "2024-09-26T14:52:16.358568Z", + "shell.execute_reply": "2024-09-26T14:52:16.358096Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "368f0547", + "id": "52d078eb", "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": "dc65d1a9", + "id": "79b5500c", "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": "e31bf904", + "id": "f114fab1", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "0365a86d", + "id": "a6fcaf91", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.495546Z", - "iopub.status.busy": "2024-09-06T19:38:13.495269Z", - "iopub.status.idle": "2024-09-06T19:38:13.502952Z", - "shell.execute_reply": "2024-09-06T19:38:13.502358Z" + "iopub.execute_input": "2024-09-26T14:52:16.360590Z", + "iopub.status.busy": "2024-09-26T14:52:16.360173Z", + "iopub.status.idle": "2024-09-26T14:52:16.368057Z", + "shell.execute_reply": "2024-09-26T14:52:16.367484Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "1c944acb", + "id": "fe87ea59", "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": "c713e4cb", + "id": "6c7bf69f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.504946Z", - "iopub.status.busy": "2024-09-06T19:38:13.504608Z", - "iopub.status.idle": "2024-09-06T19:38:13.523104Z", - "shell.execute_reply": "2024-09-06T19:38:13.522534Z" + "iopub.execute_input": "2024-09-26T14:52:16.369947Z", + "iopub.status.busy": "2024-09-26T14:52:16.369620Z", + "iopub.status.idle": "2024-09-26T14:52:16.389325Z", + "shell.execute_reply": "2024-09-26T14:52:16.388736Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "59184bfc", + "id": "c73832aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:13.525068Z", - "iopub.status.busy": "2024-09-06T19:38:13.524743Z", - "iopub.status.idle": "2024-09-06T19:38:13.528122Z", - "shell.execute_reply": "2024-09-06T19:38:13.527552Z" + "iopub.execute_input": "2024-09-26T14:52:16.391059Z", + "iopub.status.busy": "2024-09-26T14:52:16.390763Z", + "iopub.status.idle": "2024-09-26T14:52:16.394252Z", + "shell.execute_reply": "2024-09-26T14:52:16.393690Z" } }, "outputs": [ @@ -1617,12 +1617,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a20db80d8ee4c558ba192d544a0f48a": { + "01c2b1694c624296b114bf1d67d63cff": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "0b91ac3abe0a43e4b471a93ba3834871": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,7 +1693,25 @@ "width": null } }, - "0e1e83d9b67447b1a76b3a2c668a8439": { + "1c5e90fb88e44d4280704d7ea69107fc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "1f603edcaca0493985e74b52602fd4e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1728,25 +1764,46 @@ "width": null } }, - "0ea8c549fffe4418b122c5d1daacdcf9": { + "24e8217d30ae40ec8e547d17a79c5035": { "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": "" + } + }, + "4d41deb2547b43daa3622e6c0f359568": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_853967e0e61449b5b519cbd25c8830fc", + "placeholder": "​", + "style": "IPY_MODEL_70ee3acf89114ab190c8c886bcad952a", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "10e11ec38b13425280381ff5281c4450": { + "554f0bffd2414657b0244763906a1e3d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1761,16 +1818,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_98a30ff8d08f40f5a59fa3959a1bfd7a", - "IPY_MODEL_9e361a1c4f7d49d28575030ed31684b4", - "IPY_MODEL_5d93e4fbfc844d82994983ca2900ac04" + "IPY_MODEL_c8c65a735e994d1792a82e7140824616", + "IPY_MODEL_77dcb3bbbcad4620bbed6ca47c4c44db", + "IPY_MODEL_e241857383f5412490f7baca022471b6" ], - "layout": "IPY_MODEL_4c9d550f7159424fb6452da47b5cb51f", + "layout": "IPY_MODEL_af8c576b995749f49b6a3ff1a3ef7338", "tabbable": null, "tooltip": null } }, - "1989c2b222ef4983ba1d80fd96d80f9d": { + "5569a8600bc64dfebf5774ddc1b6543c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1823,23 +1880,48 @@ "width": null } }, - "278fa17d981b49c5a5afac9215c11437": { + "70745b2f7b374dbb8bf3ac849b0ce45e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1f603edcaca0493985e74b52602fd4e9", + "placeholder": "​", + "style": "IPY_MODEL_01c2b1694c624296b114bf1d67d63cff", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1566266.10it/s]" + } + }, + "70ee3acf89114ab190c8c886bcad952a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "4c9d550f7159424fb6452da47b5cb51f": { + "74ffd9bb94c34169aa80dd9d157cd10d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1892,30 +1974,33 @@ "width": null } }, - "5d93e4fbfc844d82994983ca2900ac04": { + "77dcb3bbbcad4620bbed6ca47c4c44db": { "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_0a20db80d8ee4c558ba192d544a0f48a", - "placeholder": "​", - "style": "IPY_MODEL_85eb048e0015452a98d2585ecd3acea6", + "layout": "IPY_MODEL_c8f68f3e3778452dbb68e37345ea22a9", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c7a5057a16474398af969c555e555a5d", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 908211.86it/s]" + "value": 50.0 } }, - "62148dc5598f487787910111c96b2850": { + "853967e0e61449b5b519cbd25c8830fc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1968,30 +2053,7 @@ "width": null } }, - "663eab6313474ab4b43a56ac15375332": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0e1e83d9b67447b1a76b3a2c668a8439", - "placeholder": "​", - "style": "IPY_MODEL_c8b6ee68eda04b79b9d7e8ba44708601", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "71bf8e249c8e494a8293a1368b4cde75": { + "af8c576b995749f49b6a3ff1a3ef7338": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2044,67 +2106,72 @@ "width": null } }, - "7e2d5adb59434e2081db18c696100263": { + "c7a5057a16474398af969c555e555a5d": { "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_663eab6313474ab4b43a56ac15375332", - "IPY_MODEL_cb19cecdebc048139ef9e5b0697091e8", - "IPY_MODEL_b09565a1c786456187dacb880907b06f" - ], - "layout": "IPY_MODEL_62148dc5598f487787910111c96b2850", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "85eb048e0015452a98d2585ecd3acea6": { + "c7fe0e5fdfd74b84af9f7585515c62f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fead4528991a4aafb24e155e68de7bc9", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_24e8217d30ae40ec8e547d17a79c5035", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "8a29c209506d4b1f809b0eee618845ff": { + "c8c65a735e994d1792a82e7140824616": { "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_0b91ac3abe0a43e4b471a93ba3834871", + "placeholder": "​", + "style": "IPY_MODEL_1c5e90fb88e44d4280704d7ea69107fc", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for estimating thresholds: " } }, - "8a7564586c364ea6ab0b8036f15d75de": { + "c8f68f3e3778452dbb68e37345ea22a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2157,56 +2224,31 @@ "width": null } }, - "98a30ff8d08f40f5a59fa3959a1bfd7a": { + "d70c6118368a40e3b8c24ac57cc4db26": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_cbbdcb4211b04decb44b6be6dae0e74f", - "placeholder": "​", - "style": "IPY_MODEL_0ea8c549fffe4418b122c5d1daacdcf9", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: " - } - }, - "9e361a1c4f7d49d28575030ed31684b4": { - "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_8a7564586c364ea6ab0b8036f15d75de", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_278fa17d981b49c5a5afac9215c11437", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4d41deb2547b43daa3622e6c0f359568", + "IPY_MODEL_c7fe0e5fdfd74b84af9f7585515c62f8", + "IPY_MODEL_70745b2f7b374dbb8bf3ac849b0ce45e" + ], + "layout": "IPY_MODEL_74ffd9bb94c34169aa80dd9d157cd10d", "tabbable": null, - "tooltip": null, - "value": 50.0 + "tooltip": null } }, - "b09565a1c786456187dacb880907b06f": { + "e241857383f5412490f7baca022471b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2221,15 +2263,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_71bf8e249c8e494a8293a1368b4cde75", + "layout": "IPY_MODEL_5569a8600bc64dfebf5774ddc1b6543c", "placeholder": "​", - "style": "IPY_MODEL_8a29c209506d4b1f809b0eee618845ff", + "style": "IPY_MODEL_f6897d47a5094424a94e9a8a0a058c31", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 1197722.38it/s]" + "value": " 10000/? [00:00<00:00, 1012407.73it/s]" } }, - "c8b6ee68eda04b79b9d7e8ba44708601": { + "f6897d47a5094424a94e9a8a0a058c31": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2247,33 +2289,7 @@ "text_color": null } }, - "cb19cecdebc048139ef9e5b0697091e8": { - "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_1989c2b222ef4983ba1d80fd96d80f9d", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cd96842c5f86404599e6a57c4439dccf", - "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "cbbdcb4211b04decb44b6be6dae0e74f": { + "fead4528991a4aafb24e155e68de7bc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2325,22 +2341,6 @@ "visibility": null, "width": null } - }, - "cd96842c5f86404599e6a57c4439dccf": { - "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/improving_ml_performance.ipynb b/master/tutorials/improving_ml_performance.ipynb index 0126898fa..d59d7a26c 100644 --- a/master/tutorials/improving_ml_performance.ipynb +++ b/master/tutorials/improving_ml_performance.ipynb @@ -60,10 +60,10 @@ "id": "2d638465", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:17.966921Z", - "iopub.status.busy": "2024-09-06T19:38:17.966743Z", - "iopub.status.idle": "2024-09-06T19:38:19.153643Z", - "shell.execute_reply": "2024-09-06T19:38:19.153020Z" + "iopub.execute_input": "2024-09-26T14:52:19.810405Z", + "iopub.status.busy": "2024-09-26T14:52:19.810223Z", + "iopub.status.idle": "2024-09-26T14:52:21.040404Z", + "shell.execute_reply": "2024-09-26T14:52:21.039829Z" }, "nbsphinx": "hidden" }, @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"xgboost\", \"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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -99,10 +99,10 @@ "id": "b0bbf715-47c6-44ea-b15e-89800e62ee04", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.156468Z", - "iopub.status.busy": "2024-09-06T19:38:19.155927Z", - "iopub.status.idle": "2024-09-06T19:38:19.159820Z", - "shell.execute_reply": "2024-09-06T19:38:19.159280Z" + "iopub.execute_input": "2024-09-26T14:52:21.042734Z", + "iopub.status.busy": "2024-09-26T14:52:21.042166Z", + "iopub.status.idle": "2024-09-26T14:52:21.046124Z", + "shell.execute_reply": "2024-09-26T14:52:21.045639Z" } }, "outputs": [], @@ -140,10 +140,10 @@ "id": "c58f8015-d051-411c-9e03-5659cf3ad956", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.161985Z", - "iopub.status.busy": "2024-09-06T19:38:19.161628Z", - "iopub.status.idle": "2024-09-06T19:38:19.848074Z", - "shell.execute_reply": "2024-09-06T19:38:19.847540Z" + "iopub.execute_input": "2024-09-26T14:52:21.047800Z", + "iopub.status.busy": "2024-09-26T14:52:21.047493Z", + "iopub.status.idle": "2024-09-26T14:52:21.500478Z", + "shell.execute_reply": "2024-09-26T14:52:21.499906Z" } }, "outputs": [ @@ -273,10 +273,10 @@ "id": "1b5f50e6-d125-4e61-b63e-4004f0c9099a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.850305Z", - "iopub.status.busy": "2024-09-06T19:38:19.849961Z", - "iopub.status.idle": "2024-09-06T19:38:19.855710Z", - "shell.execute_reply": "2024-09-06T19:38:19.855268Z" + "iopub.execute_input": "2024-09-26T14:52:21.502342Z", + "iopub.status.busy": "2024-09-26T14:52:21.502065Z", + "iopub.status.idle": "2024-09-26T14:52:21.509359Z", + "shell.execute_reply": "2024-09-26T14:52:21.508870Z" } }, "outputs": [], @@ -312,10 +312,10 @@ "id": "a36c21e9-1c32-4df9-bd87-fffeb8c2175f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.857664Z", - "iopub.status.busy": "2024-09-06T19:38:19.857483Z", - "iopub.status.idle": "2024-09-06T19:38:19.864510Z", - "shell.execute_reply": "2024-09-06T19:38:19.863928Z" + "iopub.execute_input": "2024-09-26T14:52:21.511294Z", + "iopub.status.busy": "2024-09-26T14:52:21.510958Z", + "iopub.status.idle": "2024-09-26T14:52:21.518230Z", + "shell.execute_reply": "2024-09-26T14:52:21.517794Z" } }, "outputs": [ @@ -418,10 +418,10 @@ "id": "5f856a3a-8aae-4836-b146-9ab68d8d1c7a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.866738Z", - "iopub.status.busy": "2024-09-06T19:38:19.866419Z", - "iopub.status.idle": "2024-09-06T19:38:19.871181Z", - "shell.execute_reply": "2024-09-06T19:38:19.870718Z" + "iopub.execute_input": "2024-09-26T14:52:21.520016Z", + "iopub.status.busy": "2024-09-26T14:52:21.519670Z", + "iopub.status.idle": "2024-09-26T14:52:21.524522Z", + "shell.execute_reply": "2024-09-26T14:52:21.524038Z" } }, "outputs": [], @@ -449,10 +449,10 @@ "id": "46275634-da56-4e58-9061-8108be2b585d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.873167Z", - "iopub.status.busy": "2024-09-06T19:38:19.872989Z", - "iopub.status.idle": "2024-09-06T19:38:19.879315Z", - "shell.execute_reply": "2024-09-06T19:38:19.878873Z" + "iopub.execute_input": "2024-09-26T14:52:21.526279Z", + "iopub.status.busy": "2024-09-26T14:52:21.525942Z", + "iopub.status.idle": "2024-09-26T14:52:21.531374Z", + "shell.execute_reply": "2024-09-26T14:52:21.530921Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "id": "769c4c5e-a7ff-4e02-bee5-2b2e676aec14", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.881299Z", - "iopub.status.busy": "2024-09-06T19:38:19.881109Z", - "iopub.status.idle": "2024-09-06T19:38:19.885448Z", - "shell.execute_reply": "2024-09-06T19:38:19.884866Z" + "iopub.execute_input": "2024-09-26T14:52:21.533093Z", + "iopub.status.busy": "2024-09-26T14:52:21.532754Z", + "iopub.status.idle": "2024-09-26T14:52:21.536654Z", + "shell.execute_reply": "2024-09-26T14:52:21.536203Z" } }, "outputs": [], @@ -506,10 +506,10 @@ "id": "7ac47c3d-9e87-45b7-9064-bfa45578872e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.887541Z", - "iopub.status.busy": "2024-09-06T19:38:19.887226Z", - "iopub.status.idle": "2024-09-06T19:38:19.952333Z", - "shell.execute_reply": "2024-09-06T19:38:19.951659Z" + "iopub.execute_input": "2024-09-26T14:52:21.538466Z", + "iopub.status.busy": "2024-09-26T14:52:21.538138Z", + "iopub.status.idle": "2024-09-26T14:52:21.605533Z", + "shell.execute_reply": "2024-09-26T14:52:21.604911Z" } }, "outputs": [ @@ -609,10 +609,10 @@ "id": "6cef169e-d15b-4d18-9cb7-8ea589557e6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.955055Z", - "iopub.status.busy": "2024-09-06T19:38:19.954571Z", - "iopub.status.idle": "2024-09-06T19:38:19.965639Z", - "shell.execute_reply": "2024-09-06T19:38:19.965092Z" + "iopub.execute_input": "2024-09-26T14:52:21.608178Z", + "iopub.status.busy": "2024-09-26T14:52:21.607735Z", + "iopub.status.idle": "2024-09-26T14:52:21.620493Z", + "shell.execute_reply": "2024-09-26T14:52:21.619924Z" } }, "outputs": [ @@ -724,10 +724,10 @@ "id": "b68e0418-86cf-431f-9107-2dd0a310ca42", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.968612Z", - "iopub.status.busy": "2024-09-06T19:38:19.968081Z", - "iopub.status.idle": "2024-09-06T19:38:19.989523Z", - "shell.execute_reply": "2024-09-06T19:38:19.988990Z" + "iopub.execute_input": "2024-09-26T14:52:21.623400Z", + "iopub.status.busy": "2024-09-26T14:52:21.622546Z", + "iopub.status.idle": "2024-09-26T14:52:21.644716Z", + "shell.execute_reply": "2024-09-26T14:52:21.644193Z" } }, "outputs": [ @@ -931,10 +931,10 @@ "id": "0e9bd131-429f-48af-b4fc-ed8b907950b9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:19.992484Z", - "iopub.status.busy": "2024-09-06T19:38:19.991953Z", - "iopub.status.idle": "2024-09-06T19:38:19.996496Z", - "shell.execute_reply": "2024-09-06T19:38:19.995963Z" + "iopub.execute_input": "2024-09-26T14:52:21.647639Z", + "iopub.status.busy": "2024-09-26T14:52:21.646753Z", + "iopub.status.idle": "2024-09-26T14:52:21.652233Z", + "shell.execute_reply": "2024-09-26T14:52:21.651741Z" } }, "outputs": [ @@ -968,10 +968,10 @@ "id": "e72320ec-7792-4347-b2fb-630f2519127c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.000004Z", - "iopub.status.busy": "2024-09-06T19:38:19.999084Z", - "iopub.status.idle": "2024-09-06T19:38:20.005225Z", - "shell.execute_reply": "2024-09-06T19:38:20.004698Z" + "iopub.execute_input": "2024-09-26T14:52:21.654600Z", + "iopub.status.busy": "2024-09-26T14:52:21.654175Z", + "iopub.status.idle": "2024-09-26T14:52:21.659391Z", + "shell.execute_reply": "2024-09-26T14:52:21.658868Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "id": "8520ba4a-3ad6-408a-b377-3f47c32d745a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.008748Z", - "iopub.status.busy": "2024-09-06T19:38:20.007824Z", - "iopub.status.idle": "2024-09-06T19:38:20.018446Z", - "shell.execute_reply": "2024-09-06T19:38:20.018010Z" + "iopub.execute_input": "2024-09-26T14:52:21.661608Z", + "iopub.status.busy": "2024-09-26T14:52:21.661407Z", + "iopub.status.idle": "2024-09-26T14:52:21.671252Z", + "shell.execute_reply": "2024-09-26T14:52:21.670825Z" } }, "outputs": [ @@ -1205,10 +1205,10 @@ "id": "3c002665-c48b-4f04-91f7-ad112a49efc7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.020571Z", - "iopub.status.busy": "2024-09-06T19:38:20.020204Z", - "iopub.status.idle": "2024-09-06T19:38:20.024666Z", - "shell.execute_reply": "2024-09-06T19:38:20.024096Z" + "iopub.execute_input": "2024-09-26T14:52:21.673132Z", + "iopub.status.busy": "2024-09-26T14:52:21.672789Z", + "iopub.status.idle": "2024-09-26T14:52:21.677167Z", + "shell.execute_reply": "2024-09-26T14:52:21.676751Z" } }, "outputs": [], @@ -1234,10 +1234,10 @@ "id": "36319f39-f563-4f63-913f-821373180350", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.026677Z", - "iopub.status.busy": "2024-09-06T19:38:20.026505Z", - "iopub.status.idle": "2024-09-06T19:38:20.138981Z", - "shell.execute_reply": "2024-09-06T19:38:20.138473Z" + "iopub.execute_input": "2024-09-26T14:52:21.678723Z", + "iopub.status.busy": "2024-09-26T14:52:21.678550Z", + "iopub.status.idle": "2024-09-26T14:52:21.827660Z", + "shell.execute_reply": "2024-09-26T14:52:21.827142Z" } }, "outputs": [ @@ -1711,10 +1711,10 @@ "id": "044c0eb1-299a-4851-b1bf-268d5bce56c1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.141251Z", - "iopub.status.busy": "2024-09-06T19:38:20.140804Z", - "iopub.status.idle": "2024-09-06T19:38:20.147269Z", - "shell.execute_reply": "2024-09-06T19:38:20.146678Z" + "iopub.execute_input": "2024-09-26T14:52:21.829459Z", + "iopub.status.busy": "2024-09-26T14:52:21.829100Z", + "iopub.status.idle": "2024-09-26T14:52:21.835627Z", + "shell.execute_reply": "2024-09-26T14:52:21.835049Z" } }, "outputs": [], @@ -1738,10 +1738,10 @@ "id": "c43df278-abfe-40e5-9d48-2df3efea9379", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:20.149710Z", - "iopub.status.busy": "2024-09-06T19:38:20.149204Z", - "iopub.status.idle": "2024-09-06T19:38:22.175679Z", - "shell.execute_reply": "2024-09-06T19:38:22.175042Z" + "iopub.execute_input": "2024-09-26T14:52:21.837607Z", + "iopub.status.busy": "2024-09-26T14:52:21.837231Z", + "iopub.status.idle": "2024-09-26T14:52:23.851625Z", + "shell.execute_reply": "2024-09-26T14:52:23.850969Z" } }, "outputs": [ @@ -1953,10 +1953,10 @@ "id": "77c7f776-54b3-45b5-9207-715d6d2e90c0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.179907Z", - "iopub.status.busy": "2024-09-06T19:38:22.178817Z", - "iopub.status.idle": "2024-09-06T19:38:22.193599Z", - "shell.execute_reply": "2024-09-06T19:38:22.193081Z" + "iopub.execute_input": "2024-09-26T14:52:23.853998Z", + "iopub.status.busy": "2024-09-26T14:52:23.853506Z", + "iopub.status.idle": "2024-09-26T14:52:23.867378Z", + "shell.execute_reply": "2024-09-26T14:52:23.866868Z" } }, "outputs": [ @@ -2073,10 +2073,10 @@ "id": "7e218d04-0729-4f42-b264-51c73601ebe6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.197201Z", - "iopub.status.busy": "2024-09-06T19:38:22.196240Z", - "iopub.status.idle": "2024-09-06T19:38:22.200280Z", - "shell.execute_reply": "2024-09-06T19:38:22.199770Z" + "iopub.execute_input": "2024-09-26T14:52:23.869442Z", + "iopub.status.busy": "2024-09-26T14:52:23.869086Z", + "iopub.status.idle": "2024-09-26T14:52:23.871992Z", + "shell.execute_reply": "2024-09-26T14:52:23.871490Z" } }, "outputs": [], @@ -2090,10 +2090,10 @@ "id": "7e2bdb41-321e-4929-aa01-1f60948b9e8b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.203753Z", - "iopub.status.busy": "2024-09-06T19:38:22.202840Z", - "iopub.status.idle": "2024-09-06T19:38:22.208375Z", - "shell.execute_reply": "2024-09-06T19:38:22.207870Z" + "iopub.execute_input": "2024-09-26T14:52:23.873901Z", + "iopub.status.busy": "2024-09-26T14:52:23.873567Z", + "iopub.status.idle": "2024-09-26T14:52:23.878299Z", + "shell.execute_reply": "2024-09-26T14:52:23.877773Z" } }, "outputs": [], @@ -2117,10 +2117,10 @@ "id": "5ce2d89f-e832-448d-bfac-9941da15c895", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.211876Z", - "iopub.status.busy": "2024-09-06T19:38:22.210955Z", - "iopub.status.idle": "2024-09-06T19:38:22.243013Z", - "shell.execute_reply": "2024-09-06T19:38:22.242528Z" + "iopub.execute_input": "2024-09-26T14:52:23.880472Z", + "iopub.status.busy": "2024-09-26T14:52:23.880009Z", + "iopub.status.idle": "2024-09-26T14:52:23.917031Z", + "shell.execute_reply": "2024-09-26T14:52:23.916497Z" } }, "outputs": [ @@ -2160,10 +2160,10 @@ "id": "9f437756-112e-4531-84fc-6ceadd0c9ef5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.246118Z", - "iopub.status.busy": "2024-09-06T19:38:22.245468Z", - "iopub.status.idle": "2024-09-06T19:38:22.754137Z", - "shell.execute_reply": "2024-09-06T19:38:22.753573Z" + "iopub.execute_input": "2024-09-26T14:52:23.919143Z", + "iopub.status.busy": "2024-09-26T14:52:23.918754Z", + "iopub.status.idle": "2024-09-26T14:52:24.441145Z", + "shell.execute_reply": "2024-09-26T14:52:24.440578Z" } }, "outputs": [], @@ -2194,10 +2194,10 @@ "id": "707625f6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.757125Z", - "iopub.status.busy": "2024-09-06T19:38:22.756730Z", - "iopub.status.idle": "2024-09-06T19:38:22.893326Z", - "shell.execute_reply": "2024-09-06T19:38:22.892578Z" + "iopub.execute_input": "2024-09-26T14:52:24.443535Z", + "iopub.status.busy": "2024-09-26T14:52:24.443148Z", + "iopub.status.idle": "2024-09-26T14:52:24.581215Z", + "shell.execute_reply": "2024-09-26T14:52:24.580592Z" } }, "outputs": [ @@ -2408,10 +2408,10 @@ "id": "25afe46c-a521-483c-b168-728c76d970dc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.896382Z", - "iopub.status.busy": "2024-09-06T19:38:22.896143Z", - "iopub.status.idle": "2024-09-06T19:38:22.903618Z", - "shell.execute_reply": "2024-09-06T19:38:22.903032Z" + "iopub.execute_input": "2024-09-26T14:52:24.583982Z", + "iopub.status.busy": "2024-09-26T14:52:24.583021Z", + "iopub.status.idle": "2024-09-26T14:52:24.591560Z", + "shell.execute_reply": "2024-09-26T14:52:24.591052Z" } }, "outputs": [ @@ -2441,10 +2441,10 @@ "id": "6efcf06f-cc40-4964-87df-5204d3b1b9d4", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.906322Z", - "iopub.status.busy": "2024-09-06T19:38:22.906102Z", - "iopub.status.idle": "2024-09-06T19:38:22.914842Z", - "shell.execute_reply": "2024-09-06T19:38:22.914319Z" + "iopub.execute_input": "2024-09-26T14:52:24.594472Z", + "iopub.status.busy": "2024-09-26T14:52:24.593722Z", + "iopub.status.idle": "2024-09-26T14:52:24.601463Z", + "shell.execute_reply": "2024-09-26T14:52:24.600918Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "7bc87d72-bbd5-4ed2-bc38-2218862ddfbd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.917418Z", - "iopub.status.busy": "2024-09-06T19:38:22.917212Z", - "iopub.status.idle": "2024-09-06T19:38:22.924586Z", - "shell.execute_reply": "2024-09-06T19:38:22.924068Z" + "iopub.execute_input": "2024-09-26T14:52:24.604404Z", + "iopub.status.busy": "2024-09-26T14:52:24.603652Z", + "iopub.status.idle": "2024-09-26T14:52:24.610627Z", + "shell.execute_reply": "2024-09-26T14:52:24.610123Z" } }, "outputs": [ @@ -2513,10 +2513,10 @@ "id": "9c70be3e-0ba2-4e3e-8c50-359d402ca1fe", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.927978Z", - "iopub.status.busy": "2024-09-06T19:38:22.927001Z", - "iopub.status.idle": "2024-09-06T19:38:22.932989Z", - "shell.execute_reply": "2024-09-06T19:38:22.932417Z" + "iopub.execute_input": "2024-09-26T14:52:24.613514Z", + "iopub.status.busy": "2024-09-26T14:52:24.612748Z", + "iopub.status.idle": "2024-09-26T14:52:24.618379Z", + "shell.execute_reply": "2024-09-26T14:52:24.617862Z" } }, "outputs": [ @@ -2542,10 +2542,10 @@ "id": "08080458-0cd7-447d-80e6-384cb8d31eaf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.935455Z", - "iopub.status.busy": "2024-09-06T19:38:22.935286Z", - "iopub.status.idle": "2024-09-06T19:38:22.940366Z", - "shell.execute_reply": "2024-09-06T19:38:22.939926Z" + "iopub.execute_input": "2024-09-26T14:52:24.621206Z", + "iopub.status.busy": "2024-09-26T14:52:24.620459Z", + "iopub.status.idle": "2024-09-26T14:52:24.625372Z", + "shell.execute_reply": "2024-09-26T14:52:24.624794Z" } }, "outputs": [], @@ -2569,10 +2569,10 @@ "id": "009bb215-4d26-47da-a230-d0ccf4122629", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:22.942577Z", - "iopub.status.busy": "2024-09-06T19:38:22.942242Z", - "iopub.status.idle": "2024-09-06T19:38:23.018404Z", - "shell.execute_reply": "2024-09-06T19:38:23.017754Z" + "iopub.execute_input": "2024-09-26T14:52:24.627070Z", + "iopub.status.busy": "2024-09-26T14:52:24.626899Z", + "iopub.status.idle": "2024-09-26T14:52:24.703448Z", + "shell.execute_reply": "2024-09-26T14:52:24.702825Z" } }, "outputs": [ @@ -3052,10 +3052,10 @@ "id": "dcaeda51-9b24-4c04-889d-7e63563594fc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.021060Z", - "iopub.status.busy": "2024-09-06T19:38:23.020492Z", - "iopub.status.idle": "2024-09-06T19:38:23.034062Z", - "shell.execute_reply": "2024-09-06T19:38:23.033451Z" + "iopub.execute_input": "2024-09-26T14:52:24.705665Z", + "iopub.status.busy": "2024-09-26T14:52:24.705281Z", + "iopub.status.idle": "2024-09-26T14:52:24.718371Z", + "shell.execute_reply": "2024-09-26T14:52:24.717910Z" } }, "outputs": [ @@ -3111,10 +3111,10 @@ "id": "1d92d78d-e4a8-4322-bf38-f5a5dae3bf17", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.036553Z", - "iopub.status.busy": "2024-09-06T19:38:23.036240Z", - "iopub.status.idle": "2024-09-06T19:38:23.039008Z", - "shell.execute_reply": "2024-09-06T19:38:23.038465Z" + "iopub.execute_input": "2024-09-26T14:52:24.719953Z", + "iopub.status.busy": "2024-09-26T14:52:24.719774Z", + "iopub.status.idle": "2024-09-26T14:52:24.722525Z", + "shell.execute_reply": "2024-09-26T14:52:24.721993Z" } }, "outputs": [], @@ -3150,10 +3150,10 @@ "id": "941ab2a6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.041147Z", - "iopub.status.busy": "2024-09-06T19:38:23.040695Z", - "iopub.status.idle": "2024-09-06T19:38:23.050646Z", - "shell.execute_reply": "2024-09-06T19:38:23.050044Z" + "iopub.execute_input": "2024-09-26T14:52:24.724217Z", + "iopub.status.busy": "2024-09-26T14:52:24.723890Z", + "iopub.status.idle": "2024-09-26T14:52:24.733856Z", + "shell.execute_reply": "2024-09-26T14:52:24.733386Z" } }, "outputs": [], @@ -3261,10 +3261,10 @@ "id": "50666fb9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.053067Z", - "iopub.status.busy": "2024-09-06T19:38:23.052637Z", - "iopub.status.idle": "2024-09-06T19:38:23.059254Z", - "shell.execute_reply": "2024-09-06T19:38:23.058781Z" + "iopub.execute_input": "2024-09-26T14:52:24.735568Z", + "iopub.status.busy": "2024-09-26T14:52:24.735390Z", + "iopub.status.idle": "2024-09-26T14:52:24.741960Z", + "shell.execute_reply": "2024-09-26T14:52:24.741508Z" }, "nbsphinx": "hidden" }, @@ -3346,10 +3346,10 @@ "id": "f5aa2883-d20d-481f-a012-fcc7ff8e3e7e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.061114Z", - "iopub.status.busy": "2024-09-06T19:38:23.060934Z", - "iopub.status.idle": "2024-09-06T19:38:23.064369Z", - "shell.execute_reply": "2024-09-06T19:38:23.063906Z" + "iopub.execute_input": "2024-09-26T14:52:24.743631Z", + "iopub.status.busy": "2024-09-26T14:52:24.743288Z", + "iopub.status.idle": "2024-09-26T14:52:24.746500Z", + "shell.execute_reply": "2024-09-26T14:52:24.746046Z" } }, "outputs": [], @@ -3373,10 +3373,10 @@ "id": "ce1c0ada-88b1-4654-b43f-3c0b59002979", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:23.066492Z", - "iopub.status.busy": "2024-09-06T19:38:23.066088Z", - "iopub.status.idle": "2024-09-06T19:38:27.075896Z", - "shell.execute_reply": "2024-09-06T19:38:27.075361Z" + "iopub.execute_input": "2024-09-26T14:52:24.748147Z", + "iopub.status.busy": "2024-09-26T14:52:24.747796Z", + "iopub.status.idle": "2024-09-26T14:52:28.830714Z", + "shell.execute_reply": "2024-09-26T14:52:28.830201Z" } }, "outputs": [ @@ -3419,10 +3419,10 @@ "id": "3f572acf-31c3-4874-9100-451796e35b06", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:27.079119Z", - "iopub.status.busy": "2024-09-06T19:38:27.078209Z", - "iopub.status.idle": "2024-09-06T19:38:27.082469Z", - "shell.execute_reply": "2024-09-06T19:38:27.082025Z" + "iopub.execute_input": "2024-09-26T14:52:28.832745Z", + "iopub.status.busy": "2024-09-26T14:52:28.832361Z", + "iopub.status.idle": "2024-09-26T14:52:28.835718Z", + "shell.execute_reply": "2024-09-26T14:52:28.835165Z" } }, "outputs": [ @@ -3460,10 +3460,10 @@ "id": "6a025a88", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:27.084613Z", - "iopub.status.busy": "2024-09-06T19:38:27.084277Z", - "iopub.status.idle": "2024-09-06T19:38:27.087400Z", - "shell.execute_reply": "2024-09-06T19:38:27.086984Z" + "iopub.execute_input": "2024-09-26T14:52:28.837752Z", + "iopub.status.busy": "2024-09-26T14:52:28.837357Z", + "iopub.status.idle": "2024-09-26T14:52:28.840312Z", + "shell.execute_reply": "2024-09-26T14:52:28.839737Z" }, "nbsphinx": "hidden" }, @@ -3492,7 +3492,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index d4d06d3f8..f81022c48 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": "2024-09-06T19:38:29.945055Z", - "iopub.status.busy": "2024-09-06T19:38:29.944859Z", - "iopub.status.idle": "2024-09-06T19:38:31.152677Z", - "shell.execute_reply": "2024-09-06T19:38:31.152154Z" + "iopub.execute_input": "2024-09-26T14:52:32.169125Z", + "iopub.status.busy": "2024-09-26T14:52:32.168956Z", + "iopub.status.idle": "2024-09-26T14:52:33.431499Z", + "shell.execute_reply": "2024-09-26T14:52:33.430884Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:31.155349Z", - "iopub.status.busy": "2024-09-06T19:38:31.154914Z", - "iopub.status.idle": "2024-09-06T19:38:31.333867Z", - "shell.execute_reply": "2024-09-06T19:38:31.333299Z" + "iopub.execute_input": "2024-09-26T14:52:33.434100Z", + "iopub.status.busy": "2024-09-26T14:52:33.433789Z", + "iopub.status.idle": "2024-09-26T14:52:33.621182Z", + "shell.execute_reply": "2024-09-26T14:52:33.620609Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.336296Z", - "iopub.status.busy": "2024-09-06T19:38:31.336106Z", - "iopub.status.idle": "2024-09-06T19:38:31.347492Z", - "shell.execute_reply": "2024-09-06T19:38:31.347045Z" + "iopub.execute_input": "2024-09-26T14:52:33.623560Z", + "iopub.status.busy": "2024-09-26T14:52:33.623109Z", + "iopub.status.idle": "2024-09-26T14:52:33.635370Z", + "shell.execute_reply": "2024-09-26T14:52:33.634790Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.349587Z", - "iopub.status.busy": "2024-09-06T19:38:31.349239Z", - "iopub.status.idle": "2024-09-06T19:38:31.559000Z", - "shell.execute_reply": "2024-09-06T19:38:31.558435Z" + "iopub.execute_input": "2024-09-26T14:52:33.637192Z", + "iopub.status.busy": "2024-09-26T14:52:33.636918Z", + "iopub.status.idle": "2024-09-26T14:52:33.875845Z", + "shell.execute_reply": "2024-09-26T14:52:33.875224Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.561389Z", - "iopub.status.busy": "2024-09-06T19:38:31.561027Z", - "iopub.status.idle": "2024-09-06T19:38:31.587035Z", - "shell.execute_reply": "2024-09-06T19:38:31.586568Z" + "iopub.execute_input": "2024-09-26T14:52:33.877984Z", + "iopub.status.busy": "2024-09-26T14:52:33.877640Z", + "iopub.status.idle": "2024-09-26T14:52:33.905047Z", + "shell.execute_reply": "2024-09-26T14:52:33.904562Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:31.589259Z", - "iopub.status.busy": "2024-09-06T19:38:31.588898Z", - "iopub.status.idle": "2024-09-06T19:38:33.659672Z", - "shell.execute_reply": "2024-09-06T19:38:33.658986Z" + "iopub.execute_input": "2024-09-26T14:52:33.906945Z", + "iopub.status.busy": "2024-09-26T14:52:33.906618Z", + "iopub.status.idle": "2024-09-26T14:52:36.066124Z", + "shell.execute_reply": "2024-09-26T14:52:36.065509Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:33.662234Z", - "iopub.status.busy": "2024-09-06T19:38:33.661770Z", - "iopub.status.idle": "2024-09-06T19:38:33.679880Z", - "shell.execute_reply": "2024-09-06T19:38:33.679304Z" + "iopub.execute_input": "2024-09-26T14:52:36.068327Z", + "iopub.status.busy": "2024-09-26T14:52:36.067791Z", + "iopub.status.idle": "2024-09-26T14:52:36.085955Z", + "shell.execute_reply": "2024-09-26T14:52:36.085444Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:33.682125Z", - "iopub.status.busy": "2024-09-06T19:38:33.681797Z", - "iopub.status.idle": "2024-09-06T19:38:35.246559Z", - "shell.execute_reply": "2024-09-06T19:38:35.245952Z" + "iopub.execute_input": "2024-09-26T14:52:36.087636Z", + "iopub.status.busy": "2024-09-26T14:52:36.087436Z", + "iopub.status.idle": "2024-09-26T14:52:37.714159Z", + "shell.execute_reply": "2024-09-26T14:52:37.713482Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.249384Z", - "iopub.status.busy": "2024-09-06T19:38:35.248692Z", - "iopub.status.idle": "2024-09-06T19:38:35.262909Z", - "shell.execute_reply": "2024-09-06T19:38:35.262437Z" + "iopub.execute_input": "2024-09-26T14:52:37.716539Z", + "iopub.status.busy": "2024-09-26T14:52:37.715812Z", + "iopub.status.idle": "2024-09-26T14:52:37.730102Z", + "shell.execute_reply": "2024-09-26T14:52:37.729543Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.265091Z", - "iopub.status.busy": "2024-09-06T19:38:35.264657Z", - "iopub.status.idle": "2024-09-06T19:38:35.347361Z", - "shell.execute_reply": "2024-09-06T19:38:35.346752Z" + "iopub.execute_input": "2024-09-26T14:52:37.731957Z", + "iopub.status.busy": "2024-09-26T14:52:37.731617Z", + "iopub.status.idle": "2024-09-26T14:52:37.821262Z", + "shell.execute_reply": "2024-09-26T14:52:37.820618Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.349859Z", - "iopub.status.busy": "2024-09-06T19:38:35.349553Z", - "iopub.status.idle": "2024-09-06T19:38:35.568160Z", - "shell.execute_reply": "2024-09-06T19:38:35.567596Z" + "iopub.execute_input": "2024-09-26T14:52:37.823300Z", + "iopub.status.busy": "2024-09-26T14:52:37.822839Z", + "iopub.status.idle": "2024-09-26T14:52:38.038920Z", + "shell.execute_reply": "2024-09-26T14:52:38.038375Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.570518Z", - "iopub.status.busy": "2024-09-06T19:38:35.570156Z", - "iopub.status.idle": "2024-09-06T19:38:35.587030Z", - "shell.execute_reply": "2024-09-06T19:38:35.586565Z" + "iopub.execute_input": "2024-09-26T14:52:38.040759Z", + "iopub.status.busy": "2024-09-26T14:52:38.040570Z", + "iopub.status.idle": "2024-09-26T14:52:38.058165Z", + "shell.execute_reply": "2024-09-26T14:52:38.057614Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.589095Z", - "iopub.status.busy": "2024-09-06T19:38:35.588739Z", - "iopub.status.idle": "2024-09-06T19:38:35.598220Z", - "shell.execute_reply": "2024-09-06T19:38:35.597755Z" + "iopub.execute_input": "2024-09-26T14:52:38.060074Z", + "iopub.status.busy": "2024-09-26T14:52:38.059687Z", + "iopub.status.idle": "2024-09-26T14:52:38.069888Z", + "shell.execute_reply": "2024-09-26T14:52:38.069309Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.600262Z", - "iopub.status.busy": "2024-09-06T19:38:35.599918Z", - "iopub.status.idle": "2024-09-06T19:38:35.692538Z", - "shell.execute_reply": "2024-09-06T19:38:35.691918Z" + "iopub.execute_input": "2024-09-26T14:52:38.071813Z", + "iopub.status.busy": "2024-09-26T14:52:38.071379Z", + "iopub.status.idle": "2024-09-26T14:52:38.170054Z", + "shell.execute_reply": "2024-09-26T14:52:38.169477Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.694934Z", - "iopub.status.busy": "2024-09-06T19:38:35.694629Z", - "iopub.status.idle": "2024-09-06T19:38:35.833017Z", - "shell.execute_reply": "2024-09-06T19:38:35.832312Z" + "iopub.execute_input": "2024-09-26T14:52:38.171925Z", + "iopub.status.busy": "2024-09-26T14:52:38.171696Z", + "iopub.status.idle": "2024-09-26T14:52:38.324224Z", + "shell.execute_reply": "2024-09-26T14:52:38.323549Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.835595Z", - "iopub.status.busy": "2024-09-06T19:38:35.835206Z", - "iopub.status.idle": "2024-09-06T19:38:35.839051Z", - "shell.execute_reply": "2024-09-06T19:38:35.838497Z" + "iopub.execute_input": "2024-09-26T14:52:38.326329Z", + "iopub.status.busy": "2024-09-26T14:52:38.325951Z", + "iopub.status.idle": "2024-09-26T14:52:38.329903Z", + "shell.execute_reply": "2024-09-26T14:52:38.329357Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.841055Z", - "iopub.status.busy": "2024-09-06T19:38:35.840887Z", - "iopub.status.idle": "2024-09-06T19:38:35.844523Z", - "shell.execute_reply": "2024-09-06T19:38:35.843987Z" + "iopub.execute_input": "2024-09-26T14:52:38.331907Z", + "iopub.status.busy": "2024-09-26T14:52:38.331482Z", + "iopub.status.idle": "2024-09-26T14:52:38.335196Z", + "shell.execute_reply": "2024-09-26T14:52:38.334746Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.846624Z", - "iopub.status.busy": "2024-09-06T19:38:35.846289Z", - "iopub.status.idle": "2024-09-06T19:38:35.883516Z", - "shell.execute_reply": "2024-09-06T19:38:35.883025Z" + "iopub.execute_input": "2024-09-26T14:52:38.336922Z", + "iopub.status.busy": "2024-09-26T14:52:38.336603Z", + "iopub.status.idle": "2024-09-26T14:52:38.376114Z", + "shell.execute_reply": "2024-09-26T14:52:38.375641Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.885707Z", - "iopub.status.busy": "2024-09-06T19:38:35.885360Z", - "iopub.status.idle": "2024-09-06T19:38:35.926415Z", - "shell.execute_reply": "2024-09-06T19:38:35.925951Z" + "iopub.execute_input": "2024-09-26T14:52:38.378083Z", + "iopub.status.busy": "2024-09-26T14:52:38.377733Z", + "iopub.status.idle": "2024-09-26T14:52:38.419996Z", + "shell.execute_reply": "2024-09-26T14:52:38.419527Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:35.928488Z", - "iopub.status.busy": "2024-09-06T19:38:35.928146Z", - "iopub.status.idle": "2024-09-06T19:38:36.031351Z", - "shell.execute_reply": "2024-09-06T19:38:36.030698Z" + "iopub.execute_input": "2024-09-26T14:52:38.421872Z", + "iopub.status.busy": "2024-09-26T14:52:38.421510Z", + "iopub.status.idle": "2024-09-26T14:52:38.531907Z", + "shell.execute_reply": "2024-09-26T14:52:38.531268Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.034301Z", - "iopub.status.busy": "2024-09-06T19:38:36.033912Z", - "iopub.status.idle": "2024-09-06T19:38:36.132017Z", - "shell.execute_reply": "2024-09-06T19:38:36.131369Z" + "iopub.execute_input": "2024-09-26T14:52:38.534145Z", + "iopub.status.busy": "2024-09-26T14:52:38.533766Z", + "iopub.status.idle": "2024-09-26T14:52:38.651268Z", + "shell.execute_reply": "2024-09-26T14:52:38.650679Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.134718Z", - "iopub.status.busy": "2024-09-06T19:38:36.134254Z", - "iopub.status.idle": "2024-09-06T19:38:36.372737Z", - "shell.execute_reply": "2024-09-06T19:38:36.372155Z" + "iopub.execute_input": "2024-09-26T14:52:38.653171Z", + "iopub.status.busy": "2024-09-26T14:52:38.652916Z", + "iopub.status.idle": "2024-09-26T14:52:38.868009Z", + "shell.execute_reply": "2024-09-26T14:52:38.867481Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.374987Z", - "iopub.status.busy": "2024-09-06T19:38:36.374694Z", - "iopub.status.idle": "2024-09-06T19:38:36.587886Z", - "shell.execute_reply": "2024-09-06T19:38:36.587278Z" + "iopub.execute_input": "2024-09-26T14:52:38.870022Z", + "iopub.status.busy": "2024-09-26T14:52:38.869668Z", + "iopub.status.idle": "2024-09-26T14:52:39.116995Z", + "shell.execute_reply": "2024-09-26T14:52:39.116409Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.590343Z", - "iopub.status.busy": "2024-09-06T19:38:36.589956Z", - "iopub.status.idle": "2024-09-06T19:38:36.595878Z", - "shell.execute_reply": "2024-09-06T19:38:36.595334Z" + "iopub.execute_input": "2024-09-26T14:52:39.119063Z", + "iopub.status.busy": "2024-09-26T14:52:39.118651Z", + "iopub.status.idle": "2024-09-26T14:52:39.124659Z", + "shell.execute_reply": "2024-09-26T14:52:39.124212Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.598057Z", - "iopub.status.busy": "2024-09-06T19:38:36.597740Z", - "iopub.status.idle": "2024-09-06T19:38:36.811700Z", - "shell.execute_reply": "2024-09-06T19:38:36.811079Z" + "iopub.execute_input": "2024-09-26T14:52:39.126372Z", + "iopub.status.busy": "2024-09-26T14:52:39.126025Z", + "iopub.status.idle": "2024-09-26T14:52:39.360620Z", + "shell.execute_reply": "2024-09-26T14:52:39.360015Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:36.813989Z", - "iopub.status.busy": "2024-09-06T19:38:36.813680Z", - "iopub.status.idle": "2024-09-06T19:38:37.873549Z", - "shell.execute_reply": "2024-09-06T19:38:37.872901Z" + "iopub.execute_input": "2024-09-26T14:52:39.362552Z", + "iopub.status.busy": "2024-09-26T14:52:39.362361Z", + "iopub.status.idle": "2024-09-26T14:52:40.445531Z", + "shell.execute_reply": "2024-09-26T14:52:40.444958Z" }, "id": "wL3ngCnuLEWd" }, @@ -2419,7 +2419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 0b05cce8c..f2aa83ef9 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:41.455901Z", - "iopub.status.busy": "2024-09-06T19:38:41.455732Z", - "iopub.status.idle": "2024-09-06T19:38:42.611358Z", - "shell.execute_reply": "2024-09-06T19:38:42.610733Z" + "iopub.execute_input": "2024-09-26T14:52:44.089068Z", + "iopub.status.busy": "2024-09-26T14:52:44.088906Z", + "iopub.status.idle": "2024-09-26T14:52:45.299550Z", + "shell.execute_reply": "2024-09-26T14:52:45.298928Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:42.614152Z", - "iopub.status.busy": "2024-09-06T19:38:42.613703Z", - "iopub.status.idle": "2024-09-06T19:38:42.617474Z", - "shell.execute_reply": "2024-09-06T19:38:42.616914Z" + "iopub.execute_input": "2024-09-26T14:52:45.301912Z", + "iopub.status.busy": "2024-09-26T14:52:45.301449Z", + "iopub.status.idle": "2024-09-26T14:52:45.304645Z", + "shell.execute_reply": "2024-09-26T14:52:45.304094Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.619686Z", - "iopub.status.busy": "2024-09-06T19:38:42.619396Z", - "iopub.status.idle": "2024-09-06T19:38:42.627253Z", - "shell.execute_reply": "2024-09-06T19:38:42.626804Z" + "iopub.execute_input": "2024-09-26T14:52:45.306413Z", + "iopub.status.busy": "2024-09-26T14:52:45.306142Z", + "iopub.status.idle": "2024-09-26T14:52:45.314151Z", + "shell.execute_reply": "2024-09-26T14:52:45.313702Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.629251Z", - "iopub.status.busy": "2024-09-06T19:38:42.628912Z", - "iopub.status.idle": "2024-09-06T19:38:42.675739Z", - "shell.execute_reply": "2024-09-06T19:38:42.675250Z" + "iopub.execute_input": "2024-09-26T14:52:45.315923Z", + "iopub.status.busy": "2024-09-26T14:52:45.315583Z", + "iopub.status.idle": "2024-09-26T14:52:45.364795Z", + "shell.execute_reply": "2024-09-26T14:52:45.364189Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.677746Z", - "iopub.status.busy": "2024-09-06T19:38:42.677566Z", - "iopub.status.idle": "2024-09-06T19:38:42.695187Z", - "shell.execute_reply": "2024-09-06T19:38:42.694600Z" + "iopub.execute_input": "2024-09-26T14:52:45.371300Z", + "iopub.status.busy": "2024-09-26T14:52:45.370858Z", + "iopub.status.idle": "2024-09-26T14:52:45.389579Z", + "shell.execute_reply": "2024-09-26T14:52:45.389064Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.697240Z", - "iopub.status.busy": "2024-09-06T19:38:42.696927Z", - "iopub.status.idle": "2024-09-06T19:38:42.700805Z", - "shell.execute_reply": "2024-09-06T19:38:42.700357Z" + "iopub.execute_input": "2024-09-26T14:52:45.391559Z", + "iopub.status.busy": "2024-09-26T14:52:45.391112Z", + "iopub.status.idle": "2024-09-26T14:52:45.395156Z", + "shell.execute_reply": "2024-09-26T14:52:45.394627Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.703011Z", - "iopub.status.busy": "2024-09-06T19:38:42.702619Z", - "iopub.status.idle": "2024-09-06T19:38:42.719152Z", - "shell.execute_reply": "2024-09-06T19:38:42.718696Z" + "iopub.execute_input": "2024-09-26T14:52:45.397035Z", + "iopub.status.busy": "2024-09-26T14:52:45.396725Z", + "iopub.status.idle": "2024-09-26T14:52:45.414290Z", + "shell.execute_reply": "2024-09-26T14:52:45.413687Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.721153Z", - "iopub.status.busy": "2024-09-06T19:38:42.720797Z", - "iopub.status.idle": "2024-09-06T19:38:42.746197Z", - "shell.execute_reply": "2024-09-06T19:38:42.745739Z" + "iopub.execute_input": "2024-09-26T14:52:45.416157Z", + "iopub.status.busy": "2024-09-26T14:52:45.415806Z", + "iopub.status.idle": "2024-09-26T14:52:45.442358Z", + "shell.execute_reply": "2024-09-26T14:52:45.441883Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:42.748111Z", - "iopub.status.busy": "2024-09-06T19:38:42.747776Z", - "iopub.status.idle": "2024-09-06T19:38:44.708904Z", - "shell.execute_reply": "2024-09-06T19:38:44.708307Z" + "iopub.execute_input": "2024-09-26T14:52:45.444293Z", + "iopub.status.busy": "2024-09-26T14:52:45.443936Z", + "iopub.status.idle": "2024-09-26T14:52:47.450691Z", + "shell.execute_reply": "2024-09-26T14:52:47.450163Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.711480Z", - "iopub.status.busy": "2024-09-06T19:38:44.710993Z", - "iopub.status.idle": "2024-09-06T19:38:44.717750Z", - "shell.execute_reply": "2024-09-06T19:38:44.717182Z" + "iopub.execute_input": "2024-09-26T14:52:47.452884Z", + "iopub.status.busy": "2024-09-26T14:52:47.452391Z", + "iopub.status.idle": "2024-09-26T14:52:47.459433Z", + "shell.execute_reply": "2024-09-26T14:52:47.458958Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.719963Z", - "iopub.status.busy": "2024-09-06T19:38:44.719631Z", - "iopub.status.idle": "2024-09-06T19:38:44.732695Z", - "shell.execute_reply": "2024-09-06T19:38:44.732259Z" + "iopub.execute_input": "2024-09-26T14:52:47.461250Z", + "iopub.status.busy": "2024-09-26T14:52:47.460913Z", + "iopub.status.idle": "2024-09-26T14:52:47.473767Z", + "shell.execute_reply": "2024-09-26T14:52:47.473271Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.734719Z", - "iopub.status.busy": "2024-09-06T19:38:44.734386Z", - "iopub.status.idle": "2024-09-06T19:38:44.740630Z", - "shell.execute_reply": "2024-09-06T19:38:44.740080Z" + "iopub.execute_input": "2024-09-26T14:52:47.475532Z", + "iopub.status.busy": "2024-09-26T14:52:47.475187Z", + "iopub.status.idle": "2024-09-26T14:52:47.481746Z", + "shell.execute_reply": "2024-09-26T14:52:47.481272Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.742715Z", - "iopub.status.busy": "2024-09-06T19:38:44.742407Z", - "iopub.status.idle": "2024-09-06T19:38:44.745203Z", - "shell.execute_reply": "2024-09-06T19:38:44.744635Z" + "iopub.execute_input": "2024-09-26T14:52:47.483691Z", + "iopub.status.busy": "2024-09-26T14:52:47.483212Z", + "iopub.status.idle": "2024-09-26T14:52:47.486088Z", + "shell.execute_reply": "2024-09-26T14:52:47.485626Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.747300Z", - "iopub.status.busy": "2024-09-06T19:38:44.746906Z", - "iopub.status.idle": "2024-09-06T19:38:44.750594Z", - "shell.execute_reply": "2024-09-06T19:38:44.750021Z" + "iopub.execute_input": "2024-09-26T14:52:47.487800Z", + "iopub.status.busy": "2024-09-26T14:52:47.487397Z", + "iopub.status.idle": "2024-09-26T14:52:47.491109Z", + "shell.execute_reply": "2024-09-26T14:52:47.490533Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.752864Z", - "iopub.status.busy": "2024-09-06T19:38:44.752447Z", - "iopub.status.idle": "2024-09-06T19:38:44.755290Z", - "shell.execute_reply": "2024-09-06T19:38:44.754743Z" + "iopub.execute_input": "2024-09-26T14:52:47.493003Z", + "iopub.status.busy": "2024-09-26T14:52:47.492607Z", + "iopub.status.idle": "2024-09-26T14:52:47.495261Z", + "shell.execute_reply": "2024-09-26T14:52:47.494806Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.757347Z", - "iopub.status.busy": "2024-09-06T19:38:44.757015Z", - "iopub.status.idle": "2024-09-06T19:38:44.761164Z", - "shell.execute_reply": "2024-09-06T19:38:44.760669Z" + "iopub.execute_input": "2024-09-26T14:52:47.497043Z", + "iopub.status.busy": "2024-09-26T14:52:47.496706Z", + "iopub.status.idle": "2024-09-26T14:52:47.500642Z", + "shell.execute_reply": "2024-09-26T14:52:47.500187Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.763225Z", - "iopub.status.busy": "2024-09-06T19:38:44.762830Z", - "iopub.status.idle": "2024-09-06T19:38:44.791503Z", - "shell.execute_reply": "2024-09-06T19:38:44.790922Z" + "iopub.execute_input": "2024-09-26T14:52:47.502313Z", + "iopub.status.busy": "2024-09-26T14:52:47.502139Z", + "iopub.status.idle": "2024-09-26T14:52:47.531332Z", + "shell.execute_reply": "2024-09-26T14:52:47.530848Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:44.793778Z", - "iopub.status.busy": "2024-09-06T19:38:44.793374Z", - "iopub.status.idle": "2024-09-06T19:38:44.798051Z", - "shell.execute_reply": "2024-09-06T19:38:44.797497Z" + "iopub.execute_input": "2024-09-26T14:52:47.533361Z", + "iopub.status.busy": "2024-09-26T14:52:47.532995Z", + "iopub.status.idle": "2024-09-26T14:52:47.537680Z", + "shell.execute_reply": "2024-09-26T14:52:47.537223Z" }, "nbsphinx": "hidden" }, @@ -1571,7 +1571,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "vscode": { "interpreter": { diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 7626ff8d8..9b60292d7 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": "2024-09-06T19:38:47.803342Z", - "iopub.status.busy": "2024-09-06T19:38:47.803172Z", - "iopub.status.idle": "2024-09-06T19:38:49.010459Z", - "shell.execute_reply": "2024-09-06T19:38:49.009894Z" + "iopub.execute_input": "2024-09-26T14:52:50.516908Z", + "iopub.status.busy": "2024-09-26T14:52:50.516724Z", + "iopub.status.idle": "2024-09-26T14:52:51.779618Z", + "shell.execute_reply": "2024-09-26T14:52:51.779002Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:38:49.013219Z", - "iopub.status.busy": "2024-09-06T19:38:49.012725Z", - "iopub.status.idle": "2024-09-06T19:38:49.210289Z", - "shell.execute_reply": "2024-09-06T19:38:49.209783Z" + "iopub.execute_input": "2024-09-26T14:52:51.781880Z", + "iopub.status.busy": "2024-09-26T14:52:51.781585Z", + "iopub.status.idle": "2024-09-26T14:52:51.979199Z", + "shell.execute_reply": "2024-09-26T14:52:51.978560Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:49.212873Z", - "iopub.status.busy": "2024-09-06T19:38:49.212501Z", - "iopub.status.idle": "2024-09-06T19:38:49.226305Z", - "shell.execute_reply": "2024-09-06T19:38:49.225843Z" + "iopub.execute_input": "2024-09-26T14:52:51.981718Z", + "iopub.status.busy": "2024-09-26T14:52:51.981227Z", + "iopub.status.idle": "2024-09-26T14:52:51.994745Z", + "shell.execute_reply": "2024-09-26T14:52:51.994150Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:49.228339Z", - "iopub.status.busy": "2024-09-06T19:38:49.227999Z", - "iopub.status.idle": "2024-09-06T19:38:51.870134Z", - "shell.execute_reply": "2024-09-06T19:38:51.869617Z" + "iopub.execute_input": "2024-09-26T14:52:51.996498Z", + "iopub.status.busy": "2024-09-26T14:52:51.996168Z", + "iopub.status.idle": "2024-09-26T14:52:54.626693Z", + "shell.execute_reply": "2024-09-26T14:52:54.626198Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:51.872305Z", - "iopub.status.busy": "2024-09-06T19:38:51.872107Z", - "iopub.status.idle": "2024-09-06T19:38:53.221496Z", - "shell.execute_reply": "2024-09-06T19:38:53.220930Z" + "iopub.execute_input": "2024-09-26T14:52:54.628558Z", + "iopub.status.busy": "2024-09-26T14:52:54.628209Z", + "iopub.status.idle": "2024-09-26T14:52:55.959728Z", + "shell.execute_reply": "2024-09-26T14:52:55.959162Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:53.223970Z", - "iopub.status.busy": "2024-09-06T19:38:53.223773Z", - "iopub.status.idle": "2024-09-06T19:38:53.227537Z", - "shell.execute_reply": "2024-09-06T19:38:53.226991Z" + "iopub.execute_input": "2024-09-26T14:52:55.962013Z", + "iopub.status.busy": "2024-09-26T14:52:55.961551Z", + "iopub.status.idle": "2024-09-26T14:52:55.965395Z", + "shell.execute_reply": "2024-09-26T14:52:55.964876Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:53.229541Z", - "iopub.status.busy": "2024-09-06T19:38:53.229360Z", - "iopub.status.idle": "2024-09-06T19:38:55.301308Z", - "shell.execute_reply": "2024-09-06T19:38:55.300645Z" + "iopub.execute_input": "2024-09-26T14:52:55.967241Z", + "iopub.status.busy": "2024-09-26T14:52:55.966882Z", + "iopub.status.idle": "2024-09-26T14:52:58.123639Z", + "shell.execute_reply": "2024-09-26T14:52:58.123040Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:55.303915Z", - "iopub.status.busy": "2024-09-06T19:38:55.303372Z", - "iopub.status.idle": "2024-09-06T19:38:55.311571Z", - "shell.execute_reply": "2024-09-06T19:38:55.311093Z" + "iopub.execute_input": "2024-09-26T14:52:58.126062Z", + "iopub.status.busy": "2024-09-26T14:52:58.125463Z", + "iopub.status.idle": "2024-09-26T14:52:58.134883Z", + "shell.execute_reply": "2024-09-26T14:52:58.134421Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:55.313528Z", - "iopub.status.busy": "2024-09-06T19:38:55.313186Z", - "iopub.status.idle": "2024-09-06T19:38:58.079187Z", - "shell.execute_reply": "2024-09-06T19:38:58.078607Z" + "iopub.execute_input": "2024-09-26T14:52:58.136727Z", + "iopub.status.busy": "2024-09-26T14:52:58.136398Z", + "iopub.status.idle": "2024-09-26T14:53:00.725562Z", + "shell.execute_reply": "2024-09-26T14:53:00.724908Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.081586Z", - "iopub.status.busy": "2024-09-06T19:38:58.081221Z", - "iopub.status.idle": "2024-09-06T19:38:58.084505Z", - "shell.execute_reply": "2024-09-06T19:38:58.083969Z" + "iopub.execute_input": "2024-09-26T14:53:00.727650Z", + "iopub.status.busy": "2024-09-26T14:53:00.727262Z", + "iopub.status.idle": "2024-09-26T14:53:00.731306Z", + "shell.execute_reply": "2024-09-26T14:53:00.730747Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.086650Z", - "iopub.status.busy": "2024-09-06T19:38:58.086312Z", - "iopub.status.idle": "2024-09-06T19:38:58.089596Z", - "shell.execute_reply": "2024-09-06T19:38:58.089116Z" + "iopub.execute_input": "2024-09-26T14:53:00.733136Z", + "iopub.status.busy": "2024-09-26T14:53:00.732824Z", + "iopub.status.idle": "2024-09-26T14:53:00.736387Z", + "shell.execute_reply": "2024-09-26T14:53:00.735914Z" } }, "outputs": [], @@ -769,10 +769,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:38:58.091573Z", - "iopub.status.busy": "2024-09-06T19:38:58.091252Z", - "iopub.status.idle": "2024-09-06T19:38:58.095249Z", - "shell.execute_reply": "2024-09-06T19:38:58.094671Z" + "iopub.execute_input": "2024-09-26T14:53:00.738211Z", + "iopub.status.busy": "2024-09-26T14:53:00.737791Z", + "iopub.status.idle": "2024-09-26T14:53:00.740949Z", + "shell.execute_reply": "2024-09-26T14:53:00.740494Z" }, "nbsphinx": "hidden" }, @@ -804,7 +804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index d7703f8af..1a465fa59 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:00.696602Z", - "iopub.status.busy": "2024-09-06T19:39:00.696186Z", - "iopub.status.idle": "2024-09-06T19:39:01.907009Z", - "shell.execute_reply": "2024-09-06T19:39:01.906453Z" + "iopub.execute_input": "2024-09-26T14:53:03.303111Z", + "iopub.status.busy": "2024-09-26T14:53:03.302931Z", + "iopub.status.idle": "2024-09-26T14:53:04.571865Z", + "shell.execute_reply": "2024-09-26T14:53:04.571288Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:01.909568Z", - "iopub.status.busy": "2024-09-06T19:39:01.909050Z", - "iopub.status.idle": "2024-09-06T19:39:04.631163Z", - "shell.execute_reply": "2024-09-06T19:39:04.630426Z" + "iopub.execute_input": "2024-09-26T14:53:04.574087Z", + "iopub.status.busy": "2024-09-26T14:53:04.573598Z", + "iopub.status.idle": "2024-09-26T14:53:06.166960Z", + "shell.execute_reply": "2024-09-26T14:53:06.166164Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.633881Z", - "iopub.status.busy": "2024-09-06T19:39:04.633499Z", - "iopub.status.idle": "2024-09-06T19:39:04.637616Z", - "shell.execute_reply": "2024-09-06T19:39:04.637024Z" + "iopub.execute_input": "2024-09-26T14:53:06.169408Z", + "iopub.status.busy": "2024-09-26T14:53:06.168985Z", + "iopub.status.idle": "2024-09-26T14:53:06.172322Z", + "shell.execute_reply": "2024-09-26T14:53:06.171868Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.639736Z", - "iopub.status.busy": "2024-09-06T19:39:04.639557Z", - "iopub.status.idle": "2024-09-06T19:39:04.646473Z", - "shell.execute_reply": "2024-09-06T19:39:04.646014Z" + "iopub.execute_input": "2024-09-26T14:53:06.174071Z", + "iopub.status.busy": "2024-09-26T14:53:06.173721Z", + "iopub.status.idle": "2024-09-26T14:53:06.180705Z", + "shell.execute_reply": "2024-09-26T14:53:06.180264Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:04.648396Z", - "iopub.status.busy": "2024-09-06T19:39:04.648219Z", - "iopub.status.idle": "2024-09-06T19:39:05.143459Z", - "shell.execute_reply": "2024-09-06T19:39:05.142840Z" + "iopub.execute_input": "2024-09-26T14:53:06.182537Z", + "iopub.status.busy": "2024-09-26T14:53:06.182190Z", + "iopub.status.idle": "2024-09-26T14:53:06.687592Z", + "shell.execute_reply": "2024-09-26T14:53:06.686965Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.146327Z", - "iopub.status.busy": "2024-09-06T19:39:05.146000Z", - "iopub.status.idle": "2024-09-06T19:39:05.151442Z", - "shell.execute_reply": "2024-09-06T19:39:05.150979Z" + "iopub.execute_input": "2024-09-26T14:53:06.689555Z", + "iopub.status.busy": "2024-09-26T14:53:06.689377Z", + "iopub.status.idle": "2024-09-26T14:53:06.695403Z", + "shell.execute_reply": "2024-09-26T14:53:06.694799Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.153485Z", - "iopub.status.busy": "2024-09-06T19:39:05.153173Z", - "iopub.status.idle": "2024-09-06T19:39:05.157137Z", - "shell.execute_reply": "2024-09-06T19:39:05.156658Z" + "iopub.execute_input": "2024-09-26T14:53:06.697090Z", + "iopub.status.busy": "2024-09-26T14:53:06.696909Z", + "iopub.status.idle": "2024-09-26T14:53:06.700584Z", + "shell.execute_reply": "2024-09-26T14:53:06.700149Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:05.159200Z", - "iopub.status.busy": "2024-09-06T19:39:05.158859Z", - "iopub.status.idle": "2024-09-06T19:39:06.019168Z", - "shell.execute_reply": "2024-09-06T19:39:06.018545Z" + "iopub.execute_input": "2024-09-26T14:53:06.702385Z", + "iopub.status.busy": "2024-09-26T14:53:06.702049Z", + "iopub.status.idle": "2024-09-26T14:53:07.596906Z", + "shell.execute_reply": "2024-09-26T14:53:07.596232Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.021668Z", - "iopub.status.busy": "2024-09-06T19:39:06.021221Z", - "iopub.status.idle": "2024-09-06T19:39:06.237090Z", - "shell.execute_reply": "2024-09-06T19:39:06.236553Z" + "iopub.execute_input": "2024-09-26T14:53:07.599111Z", + "iopub.status.busy": "2024-09-26T14:53:07.598647Z", + "iopub.status.idle": "2024-09-26T14:53:07.803313Z", + "shell.execute_reply": "2024-09-26T14:53:07.802716Z" } }, "outputs": [ @@ -627,7 +627,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered.\n" + "Pruning 0 predictions out of 138 using threshold==0.0. These predictions are no longer considered as potential candidates for identifying label issues as their similarity with the given labels is no longer considered." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] }, { @@ -660,10 +667,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.239343Z", - "iopub.status.busy": "2024-09-06T19:39:06.238930Z", - "iopub.status.idle": "2024-09-06T19:39:06.243194Z", - "shell.execute_reply": "2024-09-06T19:39:06.242735Z" + "iopub.execute_input": "2024-09-26T14:53:07.805415Z", + "iopub.status.busy": "2024-09-26T14:53:07.804927Z", + "iopub.status.idle": "2024-09-26T14:53:07.809280Z", + "shell.execute_reply": "2024-09-26T14:53:07.808847Z" } }, "outputs": [ @@ -700,10 +707,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.245282Z", - "iopub.status.busy": "2024-09-06T19:39:06.244951Z", - "iopub.status.idle": "2024-09-06T19:39:06.697627Z", - "shell.execute_reply": "2024-09-06T19:39:06.697015Z" + "iopub.execute_input": "2024-09-26T14:53:07.810942Z", + "iopub.status.busy": "2024-09-26T14:53:07.810764Z", + "iopub.status.idle": "2024-09-26T14:53:08.277163Z", + "shell.execute_reply": "2024-09-26T14:53:08.276574Z" } }, "outputs": [ @@ -762,10 +769,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:06.700924Z", - "iopub.status.busy": "2024-09-06T19:39:06.700539Z", - "iopub.status.idle": "2024-09-06T19:39:07.035472Z", - "shell.execute_reply": "2024-09-06T19:39:07.034925Z" + "iopub.execute_input": "2024-09-26T14:53:08.279934Z", + "iopub.status.busy": "2024-09-26T14:53:08.279727Z", + "iopub.status.idle": "2024-09-26T14:53:08.615867Z", + "shell.execute_reply": "2024-09-26T14:53:08.615304Z" } }, "outputs": [ @@ -812,10 +819,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.038382Z", - "iopub.status.busy": "2024-09-06T19:39:07.038001Z", - "iopub.status.idle": "2024-09-06T19:39:07.401507Z", - "shell.execute_reply": "2024-09-06T19:39:07.400918Z" + "iopub.execute_input": "2024-09-26T14:53:08.617985Z", + "iopub.status.busy": "2024-09-26T14:53:08.617788Z", + "iopub.status.idle": "2024-09-26T14:53:08.987995Z", + "shell.execute_reply": "2024-09-26T14:53:08.987382Z" } }, "outputs": [ @@ -862,10 +869,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.404511Z", - "iopub.status.busy": "2024-09-06T19:39:07.404090Z", - "iopub.status.idle": "2024-09-06T19:39:07.846501Z", - "shell.execute_reply": "2024-09-06T19:39:07.845952Z" + "iopub.execute_input": "2024-09-26T14:53:08.990870Z", + "iopub.status.busy": "2024-09-26T14:53:08.990636Z", + "iopub.status.idle": "2024-09-26T14:53:09.438626Z", + "shell.execute_reply": "2024-09-26T14:53:09.438065Z" } }, "outputs": [ @@ -925,10 +932,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:07.851154Z", - "iopub.status.busy": "2024-09-06T19:39:07.850706Z", - "iopub.status.idle": "2024-09-06T19:39:08.296657Z", - "shell.execute_reply": "2024-09-06T19:39:08.296063Z" + "iopub.execute_input": "2024-09-26T14:53:09.442663Z", + "iopub.status.busy": "2024-09-26T14:53:09.442289Z", + "iopub.status.idle": "2024-09-26T14:53:09.875533Z", + "shell.execute_reply": "2024-09-26T14:53:09.874886Z" } }, "outputs": [ @@ -971,10 +978,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.300087Z", - "iopub.status.busy": "2024-09-06T19:39:08.299623Z", - "iopub.status.idle": "2024-09-06T19:39:08.513354Z", - "shell.execute_reply": "2024-09-06T19:39:08.512755Z" + "iopub.execute_input": "2024-09-26T14:53:09.878235Z", + "iopub.status.busy": "2024-09-26T14:53:09.877876Z", + "iopub.status.idle": "2024-09-26T14:53:10.074349Z", + "shell.execute_reply": "2024-09-26T14:53:10.073721Z" } }, "outputs": [ @@ -1017,10 +1024,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.515572Z", - "iopub.status.busy": "2024-09-06T19:39:08.515168Z", - "iopub.status.idle": "2024-09-06T19:39:08.694654Z", - "shell.execute_reply": "2024-09-06T19:39:08.694085Z" + "iopub.execute_input": "2024-09-26T14:53:10.076454Z", + "iopub.status.busy": "2024-09-26T14:53:10.076093Z", + "iopub.status.idle": "2024-09-26T14:53:10.258000Z", + "shell.execute_reply": "2024-09-26T14:53:10.257430Z" } }, "outputs": [ @@ -1067,10 +1074,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.697419Z", - "iopub.status.busy": "2024-09-06T19:39:08.697030Z", - "iopub.status.idle": "2024-09-06T19:39:08.699909Z", - "shell.execute_reply": "2024-09-06T19:39:08.699453Z" + "iopub.execute_input": "2024-09-26T14:53:10.260221Z", + "iopub.status.busy": "2024-09-26T14:53:10.259868Z", + "iopub.status.idle": "2024-09-26T14:53:10.262670Z", + "shell.execute_reply": "2024-09-26T14:53:10.262238Z" } }, "outputs": [], @@ -1090,10 +1097,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:08.701948Z", - "iopub.status.busy": "2024-09-06T19:39:08.701622Z", - "iopub.status.idle": "2024-09-06T19:39:09.635839Z", - "shell.execute_reply": "2024-09-06T19:39:09.635227Z" + "iopub.execute_input": "2024-09-26T14:53:10.264357Z", + "iopub.status.busy": "2024-09-26T14:53:10.264032Z", + "iopub.status.idle": "2024-09-26T14:53:11.303194Z", + "shell.execute_reply": "2024-09-26T14:53:11.302561Z" } }, "outputs": [ @@ -1172,10 +1179,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.637949Z", - "iopub.status.busy": "2024-09-06T19:39:09.637773Z", - "iopub.status.idle": "2024-09-06T19:39:09.767317Z", - "shell.execute_reply": "2024-09-06T19:39:09.766833Z" + "iopub.execute_input": "2024-09-26T14:53:11.305028Z", + "iopub.status.busy": "2024-09-26T14:53:11.304725Z", + "iopub.status.idle": "2024-09-26T14:53:11.509799Z", + "shell.execute_reply": "2024-09-26T14:53:11.509285Z" } }, "outputs": [ @@ -1214,10 +1221,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.769238Z", - "iopub.status.busy": "2024-09-06T19:39:09.769067Z", - "iopub.status.idle": "2024-09-06T19:39:09.969227Z", - "shell.execute_reply": "2024-09-06T19:39:09.968617Z" + "iopub.execute_input": "2024-09-26T14:53:11.511395Z", + "iopub.status.busy": "2024-09-26T14:53:11.511212Z", + "iopub.status.idle": "2024-09-26T14:53:11.718820Z", + "shell.execute_reply": "2024-09-26T14:53:11.718199Z" } }, "outputs": [], @@ -1266,10 +1273,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:09.971377Z", - "iopub.status.busy": "2024-09-06T19:39:09.971032Z", - "iopub.status.idle": "2024-09-06T19:39:10.691109Z", - "shell.execute_reply": "2024-09-06T19:39:10.690570Z" + "iopub.execute_input": "2024-09-26T14:53:11.720947Z", + "iopub.status.busy": "2024-09-26T14:53:11.720765Z", + "iopub.status.idle": "2024-09-26T14:53:12.421538Z", + "shell.execute_reply": "2024-09-26T14:53:12.420820Z" } }, "outputs": [ @@ -1351,10 +1358,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:10.693528Z", - "iopub.status.busy": "2024-09-06T19:39:10.693149Z", - "iopub.status.idle": "2024-09-06T19:39:10.697005Z", - "shell.execute_reply": "2024-09-06T19:39:10.696512Z" + "iopub.execute_input": "2024-09-26T14:53:12.423286Z", + "iopub.status.busy": "2024-09-26T14:53:12.423091Z", + "iopub.status.idle": "2024-09-26T14:53:12.427074Z", + "shell.execute_reply": "2024-09-26T14:53:12.426599Z" }, "nbsphinx": "hidden" }, @@ -1387,7 +1394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 16e86eb51..b18d3d052 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -784,7 +784,7 @@

2. Pre-process the Cifar10 dataset
-100%|██████████| 170498071/170498071 [00:03<00:00, 46456493.64it/s]
+100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]
 

-
+
@@ -1134,7 +1134,7 @@

Spending too much time on data quality?Cleanlab Studio – an automated platform to find and fix issues in your dataset, 100x faster and more accurately. Cleanlab Studio automatically runs optimized data quality algorithms from this package on top of cutting-edge AutoML & Foundation models fit to your data, and helps you fix detected issues via a smart data correction interface. Try it for free!

The modern AI pipeline automated with Cleanlab Studio

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index ab02f6a16..82b2532b4 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:13.100046Z", - "iopub.status.busy": "2024-09-06T19:39:13.099622Z", - "iopub.status.idle": "2024-09-06T19:39:15.925691Z", - "shell.execute_reply": "2024-09-06T19:39:15.925058Z" + "iopub.execute_input": "2024-09-26T14:53:14.827019Z", + "iopub.status.busy": "2024-09-26T14:53:14.826845Z", + "iopub.status.idle": "2024-09-26T14:53:17.796587Z", + "shell.execute_reply": "2024-09-26T14:53:17.795936Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:15.928762Z", - "iopub.status.busy": "2024-09-06T19:39:15.928196Z", - "iopub.status.idle": "2024-09-06T19:39:16.252610Z", - "shell.execute_reply": "2024-09-06T19:39:16.252054Z" + "iopub.execute_input": "2024-09-26T14:53:17.798905Z", + "iopub.status.busy": "2024-09-26T14:53:17.798584Z", + "iopub.status.idle": "2024-09-26T14:53:18.137749Z", + "shell.execute_reply": "2024-09-26T14:53:18.137173Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:16.255233Z", - "iopub.status.busy": "2024-09-06T19:39:16.254751Z", - "iopub.status.idle": "2024-09-06T19:39:16.259089Z", - "shell.execute_reply": "2024-09-06T19:39:16.258660Z" + "iopub.execute_input": "2024-09-26T14:53:18.139715Z", + "iopub.status.busy": "2024-09-26T14:53:18.139407Z", + "iopub.status.idle": "2024-09-26T14:53:18.143870Z", + "shell.execute_reply": "2024-09-26T14:53:18.143450Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:16.261376Z", - "iopub.status.busy": "2024-09-06T19:39:16.260945Z", - "iopub.status.idle": "2024-09-06T19:39:23.300858Z", - "shell.execute_reply": "2024-09-06T19:39:23.300244Z" + "iopub.execute_input": "2024-09-26T14:53:18.145657Z", + "iopub.status.busy": "2024-09-26T14:53:18.145384Z", + "iopub.status.idle": "2024-09-26T14:53:24.392739Z", + "shell.execute_reply": "2024-09-26T14:53:24.392209Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 32768/170498071 [00:00<09:50, 288460.96it/s]" + " 1%| | 1212416/170498071 [00:00<00:14, 12024376.95it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 229376/170498071 [00:00<02:31, 1124759.70it/s]" + " 4%|▎ | 6160384/170498071 [00:00<00:04, 33857865.84it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 884736/170498071 [00:00<00:52, 3225591.40it/s]" + " 6%|▌ | 10518528/170498071 [00:00<00:04, 38150209.28it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 3571712/170498071 [00:00<00:14, 11574707.14it/s]" + " 9%|▉ | 15400960/170498071 [00:00<00:03, 42330857.28it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9633792/170498071 [00:00<00:06, 25807611.79it/s]" + " 12%|█▏ | 20250624/170498071 [00:00<00:03, 44424970.42it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 15892480/170498071 [00:00<00:04, 35393042.76it/s]" + " 15%|█▍ | 24739840/170498071 [00:00<00:03, 44347437.97it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 22052864/170498071 [00:00<00:03, 41375940.12it/s]" + " 17%|█▋ | 29294592/170498071 [00:00<00:03, 44719226.63it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 27918336/170498071 [00:00<00:03, 46336247.02it/s]" + " 20%|██ | 34144256/170498071 [00:00<00:02, 45791541.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 32604160/170498071 [00:01<00:03, 45410241.06it/s]" + " 23%|██▎ | 38731776/170498071 [00:00<00:02, 45062771.26it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37978112/170498071 [00:01<00:02, 46512554.13it/s]" + " 25%|██▌ | 43253760/170498071 [00:01<00:02, 45089662.43it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 44072960/170498071 [00:01<00:02, 50196826.35it/s]" + " 28%|██▊ | 47874048/170498071 [00:01<00:02, 45213443.22it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49217536/170498071 [00:01<00:02, 50515326.91it/s]" + " 31%|███ | 52494336/170498071 [00:01<00:02, 45379651.12it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 54296576/170498071 [00:01<00:02, 49331301.44it/s]" + " 33%|███▎ | 57049088/170498071 [00:01<00:02, 44930180.76it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 60129280/170498071 [00:01<00:02, 51745509.08it/s]" + " 36%|███▌ | 61571072/170498071 [00:01<00:02, 43892355.61it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 65339392/170498071 [00:01<00:02, 51498978.62it/s]" + " 39%|███▊ | 65994752/170498071 [00:01<00:02, 43763301.63it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70516736/170498071 [00:01<00:01, 50172708.54it/s]" + " 41%|████▏ | 70385664/170498071 [00:01<00:02, 43438744.69it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76251136/170498071 [00:01<00:01, 52173671.62it/s]" + " 44%|████▍ | 75104256/170498071 [00:01<00:02, 44425115.39it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 81559552/170498071 [00:01<00:01, 52429909.15it/s]" + " 47%|████▋ | 79855616/170498071 [00:01<00:02, 45166993.85it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 86835200/170498071 [00:02<00:01, 50316420.17it/s]" + " 49%|████▉ | 84377600/170498071 [00:01<00:01, 43789983.48it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 92438528/170498071 [00:02<00:01, 51729464.30it/s]" + " 52%|█████▏ | 88768512/170498071 [00:02<00:01, 43106787.26it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 97878016/170498071 [00:02<00:01, 52469802.74it/s]" + " 55%|█████▍ | 93093888/170498071 [00:02<00:01, 42763173.18it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 103153664/170498071 [00:02<00:01, 51263628.20it/s]" + " 57%|█████▋ | 97386496/170498071 [00:02<00:01, 42678693.69it/s]" ] }, { @@ -428,7 +428,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 108396544/170498071 [00:02<00:01, 51439851.19it/s]" + " 60%|█████▉ | 101679104/170498071 [00:02<00:01, 42558052.16it/s]" ] }, { @@ -436,7 +436,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 114130944/170498071 [00:02<00:01, 53113973.23it/s]" + " 62%|██████▏ | 106102784/170498071 [00:02<00:01, 43049601.15it/s]" ] }, { @@ -444,7 +444,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 119472128/170498071 [00:02<00:00, 51879482.02it/s]" + " 65%|██████▍ | 110592000/170498071 [00:02<00:01, 43553293.68it/s]" ] }, { @@ -452,7 +452,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 124682240/170498071 [00:02<00:00, 50047274.18it/s]" + " 67%|██████▋ | 114950144/170498071 [00:02<00:01, 43398814.53it/s]" ] }, { @@ -460,7 +460,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 130547712/170498071 [00:02<00:00, 52494107.90it/s]" + " 70%|██████▉ | 119308288/170498071 [00:02<00:01, 43218682.93it/s]" ] }, { @@ -468,7 +468,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 135823360/170498071 [00:03<00:00, 52004524.51it/s]" + " 73%|███████▎ | 125075456/170498071 [00:02<00:00, 47414945.57it/s]" ] }, { @@ -476,7 +476,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141066240/170498071 [00:03<00:00, 50983301.18it/s]" + " 78%|███████▊ | 133234688/170498071 [00:02<00:00, 57528916.08it/s]" ] }, { @@ -484,7 +484,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 146636800/170498071 [00:03<00:00, 52034590.57it/s]" + " 83%|████████▎ | 141262848/170498071 [00:03<00:00, 64272093.96it/s]" ] }, { @@ -492,7 +492,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 151879680/170498071 [00:03<00:00, 52140968.39it/s]" + " 87%|████████▋ | 149127168/170498071 [00:03<00:00, 68499385.74it/s]" ] }, { @@ -500,7 +500,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 157122560/170498071 [00:03<00:00, 50962142.96it/s]" + " 92%|█████████▏| 157024256/170498071 [00:03<00:00, 71592148.49it/s]" ] }, { @@ -508,7 +508,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 162463744/170498071 [00:03<00:00, 51228143.58it/s]" + " 97%|█████████▋| 165117952/170498071 [00:03<00:00, 74385700.68it/s]" ] }, { @@ -516,15 +516,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▊| 168329216/170498071 [00:03<00:00, 53366850.10it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:03<00:00, 46456493.64it/s]" + "100%|██████████| 170498071/170498071 [00:03<00:00, 49690890.85it/s]" ] }, { @@ -642,10 +634,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.303328Z", - "iopub.status.busy": "2024-09-06T19:39:23.302943Z", - "iopub.status.idle": "2024-09-06T19:39:23.307938Z", - "shell.execute_reply": "2024-09-06T19:39:23.307365Z" + "iopub.execute_input": "2024-09-26T14:53:24.394624Z", + "iopub.status.busy": "2024-09-26T14:53:24.394340Z", + "iopub.status.idle": "2024-09-26T14:53:24.399279Z", + "shell.execute_reply": "2024-09-26T14:53:24.398789Z" }, "nbsphinx": "hidden" }, @@ -696,10 +688,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.310122Z", - "iopub.status.busy": "2024-09-06T19:39:23.309822Z", - "iopub.status.idle": "2024-09-06T19:39:23.850296Z", - "shell.execute_reply": "2024-09-06T19:39:23.849793Z" + "iopub.execute_input": "2024-09-26T14:53:24.400938Z", + "iopub.status.busy": "2024-09-26T14:53:24.400609Z", + "iopub.status.idle": "2024-09-26T14:53:24.953810Z", + "shell.execute_reply": "2024-09-26T14:53:24.953168Z" } }, "outputs": [ @@ -732,10 +724,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:23.852466Z", - "iopub.status.busy": "2024-09-06T19:39:23.852115Z", - "iopub.status.idle": "2024-09-06T19:39:24.358610Z", - "shell.execute_reply": "2024-09-06T19:39:24.358030Z" + "iopub.execute_input": "2024-09-26T14:53:24.955849Z", + "iopub.status.busy": "2024-09-26T14:53:24.955452Z", + "iopub.status.idle": "2024-09-26T14:53:25.472907Z", + "shell.execute_reply": "2024-09-26T14:53:25.472351Z" } }, "outputs": [ @@ -773,10 +765,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:24.360839Z", - "iopub.status.busy": "2024-09-06T19:39:24.360464Z", - "iopub.status.idle": "2024-09-06T19:39:24.363781Z", - "shell.execute_reply": "2024-09-06T19:39:24.363295Z" + "iopub.execute_input": "2024-09-26T14:53:25.474962Z", + "iopub.status.busy": "2024-09-26T14:53:25.474606Z", + "iopub.status.idle": "2024-09-26T14:53:25.478282Z", + "shell.execute_reply": "2024-09-26T14:53:25.477855Z" } }, "outputs": [], @@ -799,17 +791,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:24.365783Z", - "iopub.status.busy": "2024-09-06T19:39:24.365442Z", - "iopub.status.idle": "2024-09-06T19:39:36.716347Z", - "shell.execute_reply": "2024-09-06T19:39:36.715721Z" + "iopub.execute_input": "2024-09-26T14:53:25.479985Z", + "iopub.status.busy": "2024-09-26T14:53:25.479646Z", + "iopub.status.idle": "2024-09-26T14:53:38.119311Z", + "shell.execute_reply": "2024-09-26T14:53:38.118760Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ceaa047f5ed4611b974d3fa414e2507", + "model_id": "502208beacbc4eb2877f50728ccb04c0", "version_major": 2, "version_minor": 0 }, @@ -868,10 +860,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:36.718898Z", - "iopub.status.busy": "2024-09-06T19:39:36.718487Z", - "iopub.status.idle": "2024-09-06T19:39:38.825920Z", - "shell.execute_reply": "2024-09-06T19:39:38.825316Z" + "iopub.execute_input": "2024-09-26T14:53:38.121453Z", + "iopub.status.busy": "2024-09-26T14:53:38.121019Z", + "iopub.status.idle": "2024-09-26T14:53:40.226608Z", + "shell.execute_reply": "2024-09-26T14:53:40.226078Z" } }, "outputs": [ @@ -915,10 +907,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:38.828812Z", - "iopub.status.busy": "2024-09-06T19:39:38.828333Z", - "iopub.status.idle": "2024-09-06T19:39:39.084401Z", - "shell.execute_reply": "2024-09-06T19:39:39.083812Z" + "iopub.execute_input": "2024-09-26T14:53:40.228769Z", + "iopub.status.busy": "2024-09-26T14:53:40.228334Z", + "iopub.status.idle": "2024-09-26T14:53:40.460757Z", + "shell.execute_reply": "2024-09-26T14:53:40.459979Z" } }, "outputs": [ @@ -954,10 +946,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:39.087122Z", - "iopub.status.busy": "2024-09-06T19:39:39.086611Z", - "iopub.status.idle": "2024-09-06T19:39:39.754107Z", - "shell.execute_reply": "2024-09-06T19:39:39.753534Z" + "iopub.execute_input": "2024-09-26T14:53:40.462963Z", + "iopub.status.busy": "2024-09-26T14:53:40.462510Z", + "iopub.status.idle": "2024-09-26T14:53:41.139530Z", + "shell.execute_reply": "2024-09-26T14:53:41.138920Z" } }, "outputs": [ @@ -1007,10 +999,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:39.756937Z", - "iopub.status.busy": "2024-09-06T19:39:39.756623Z", - "iopub.status.idle": "2024-09-06T19:39:40.092242Z", - "shell.execute_reply": "2024-09-06T19:39:40.091655Z" + "iopub.execute_input": "2024-09-26T14:53:41.141576Z", + "iopub.status.busy": "2024-09-26T14:53:41.141387Z", + "iopub.status.idle": "2024-09-26T14:53:41.442674Z", + "shell.execute_reply": "2024-09-26T14:53:41.442054Z" } }, "outputs": [ @@ -1058,10 +1050,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.094221Z", - "iopub.status.busy": "2024-09-06T19:39:40.094058Z", - "iopub.status.idle": "2024-09-06T19:39:40.335215Z", - "shell.execute_reply": "2024-09-06T19:39:40.334660Z" + "iopub.execute_input": "2024-09-26T14:53:41.444606Z", + "iopub.status.busy": "2024-09-26T14:53:41.444407Z", + "iopub.status.idle": "2024-09-26T14:53:41.692450Z", + "shell.execute_reply": "2024-09-26T14:53:41.691834Z" } }, "outputs": [ @@ -1117,10 +1109,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.337846Z", - "iopub.status.busy": "2024-09-06T19:39:40.337645Z", - "iopub.status.idle": "2024-09-06T19:39:40.434888Z", - "shell.execute_reply": "2024-09-06T19:39:40.434380Z" + "iopub.execute_input": "2024-09-26T14:53:41.694792Z", + "iopub.status.busy": "2024-09-26T14:53:41.694309Z", + "iopub.status.idle": "2024-09-26T14:53:41.786453Z", + "shell.execute_reply": "2024-09-26T14:53:41.785871Z" } }, "outputs": [], @@ -1141,10 +1133,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:40.437135Z", - "iopub.status.busy": "2024-09-06T19:39:40.436969Z", - "iopub.status.idle": "2024-09-06T19:39:50.846992Z", - "shell.execute_reply": "2024-09-06T19:39:50.846365Z" + "iopub.execute_input": "2024-09-26T14:53:41.788692Z", + "iopub.status.busy": "2024-09-26T14:53:41.788289Z", + "iopub.status.idle": "2024-09-26T14:53:52.391383Z", + "shell.execute_reply": "2024-09-26T14:53:52.390803Z" } }, "outputs": [ @@ -1181,10 +1173,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:50.849274Z", - "iopub.status.busy": "2024-09-06T19:39:50.849079Z", - "iopub.status.idle": "2024-09-06T19:39:53.085840Z", - "shell.execute_reply": "2024-09-06T19:39:53.085209Z" + "iopub.execute_input": "2024-09-26T14:53:52.393513Z", + "iopub.status.busy": "2024-09-26T14:53:52.393049Z", + "iopub.status.idle": "2024-09-26T14:53:54.671283Z", + "shell.execute_reply": "2024-09-26T14:53:54.670780Z" } }, "outputs": [ @@ -1215,10 +1207,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.088386Z", - "iopub.status.busy": "2024-09-06T19:39:53.087986Z", - "iopub.status.idle": "2024-09-06T19:39:53.295938Z", - "shell.execute_reply": "2024-09-06T19:39:53.295309Z" + "iopub.execute_input": "2024-09-26T14:53:54.673751Z", + "iopub.status.busy": "2024-09-26T14:53:54.673100Z", + "iopub.status.idle": "2024-09-26T14:53:54.874229Z", + "shell.execute_reply": "2024-09-26T14:53:54.873718Z" } }, "outputs": [], @@ -1232,10 +1224,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.298578Z", - "iopub.status.busy": "2024-09-06T19:39:53.298149Z", - "iopub.status.idle": "2024-09-06T19:39:53.301396Z", - "shell.execute_reply": "2024-09-06T19:39:53.300847Z" + "iopub.execute_input": "2024-09-26T14:53:54.876098Z", + "iopub.status.busy": "2024-09-26T14:53:54.875918Z", + "iopub.status.idle": "2024-09-26T14:53:54.879013Z", + "shell.execute_reply": "2024-09-26T14:53:54.878602Z" } }, "outputs": [], @@ -1273,10 +1265,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:53.303545Z", - "iopub.status.busy": "2024-09-06T19:39:53.303235Z", - "iopub.status.idle": "2024-09-06T19:39:53.311553Z", - "shell.execute_reply": "2024-09-06T19:39:53.311013Z" + "iopub.execute_input": "2024-09-26T14:53:54.880796Z", + "iopub.status.busy": "2024-09-26T14:53:54.880464Z", + "iopub.status.idle": "2024-09-26T14:53:54.888465Z", + "shell.execute_reply": "2024-09-26T14:53:54.888011Z" }, "nbsphinx": "hidden" }, @@ -1316,12 +1308,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "2a68a2d432424faba9fe0b5e6944b5e9": { + "19f8d7cfcb2441f39ec909950206b100": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1374,47 +1366,30 @@ "width": null } }, - "3ceaa047f5ed4611b974d3fa414e2507": { + "3dbde950338e4819980320793264b8f6": { "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_c213a022f9994559b2b3155f2f77656c", - "IPY_MODEL_d04af2b6417a48e88c2bb6ac7a1a352f", - "IPY_MODEL_d330cb5a3ec245d28c20140821dff479" - ], - "layout": "IPY_MODEL_8965ea1fe0204e49bbde2ee4ed6b5dbe", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_444a8341757540238acd548381d3cf78", + "placeholder": "​", + "style": "IPY_MODEL_c63ffa48637c4cf790d73142dcbf1bca", "tabbable": null, - "tooltip": null - } - }, - "653de3cf6239488fa0adf55f2a1ae049": { - "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": "model.safetensors: 100%" } }, - "8965ea1fe0204e49bbde2ee4ed6b5dbe": { + "444a8341757540238acd548381d3cf78": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1467,7 +1442,91 @@ "width": null } }, - "b06f361a24974d5a8b8c89476e47f817": { + "4b5509bd08094575af9bfd6e1b39af74": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "502208beacbc4eb2877f50728ccb04c0": { + "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_3dbde950338e4819980320793264b8f6", + "IPY_MODEL_55906b2e5cc7451a90a629cf8eaf9dfa", + "IPY_MODEL_e08a4a5cd5a34519a67999d955a20b6a" + ], + "layout": "IPY_MODEL_f0529d443cdc4ef783433718b133c35d", + "tabbable": null, + "tooltip": null + } + }, + "55906b2e5cc7451a90a629cf8eaf9dfa": { + "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_a6577ec2ef7f4efc9edc61cb0c210c81", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6428955d0d764798b409d0eed1cd24c0", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "6428955d0d764798b409d0eed1cd24c0": { + "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": "" + } + }, + "a6577ec2ef7f4efc9edc61cb0c210c81": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1520,7 +1579,7 @@ "width": null } }, - "b895588a207f4f0ca89d7c4764c3d066": { + "c63ffa48637c4cf790d73142dcbf1bca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1538,7 +1597,7 @@ "text_color": null } }, - "c213a022f9994559b2b3155f2f77656c": { + "e08a4a5cd5a34519a67999d955a20b6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1553,15 +1612,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ccda1205dcc748f99c76cf1800b182ef", + "layout": "IPY_MODEL_19f8d7cfcb2441f39ec909950206b100", "placeholder": "​", - "style": "IPY_MODEL_b895588a207f4f0ca89d7c4764c3d066", + "style": "IPY_MODEL_4b5509bd08094575af9bfd6e1b39af74", "tabbable": null, "tooltip": null, - "value": "model.safetensors: 100%" + "value": " 102M/102M [00:00<00:00, 297MB/s]" } }, - "ccda1205dcc748f99c76cf1800b182ef": { + "f0529d443cdc4ef783433718b133c35d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1613,73 +1672,6 @@ "visibility": null, "width": null } - }, - "cd03cf3d325849b9a2597fce8db90de1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d04af2b6417a48e88c2bb6ac7a1a352f": { - "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_2a68a2d432424faba9fe0b5e6944b5e9", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_653de3cf6239488fa0adf55f2a1ae049", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "d330cb5a3ec245d28c20140821dff479": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b06f361a24974d5a8b8c89476e47f817", - "placeholder": "​", - "style": "IPY_MODEL_cd03cf3d325849b9a2597fce8db90de1", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 304MB/s]" - } } }, "version_major": 2, diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 4e72a9c31..5670e5e42 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:57.671183Z", - "iopub.status.busy": "2024-09-06T19:39:57.671012Z", - "iopub.status.idle": "2024-09-06T19:39:58.889426Z", - "shell.execute_reply": "2024-09-06T19:39:58.888863Z" + "iopub.execute_input": "2024-09-26T14:53:59.188556Z", + "iopub.status.busy": "2024-09-26T14:53:59.188370Z", + "iopub.status.idle": "2024-09-26T14:54:00.464944Z", + "shell.execute_reply": "2024-09-26T14:54:00.464378Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:39:58.892009Z", - "iopub.status.busy": "2024-09-06T19:39:58.891558Z", - "iopub.status.idle": "2024-09-06T19:39:58.909420Z", - "shell.execute_reply": "2024-09-06T19:39:58.908966Z" + "iopub.execute_input": "2024-09-26T14:54:00.467202Z", + "iopub.status.busy": "2024-09-26T14:54:00.466665Z", + "iopub.status.idle": "2024-09-26T14:54:00.486020Z", + "shell.execute_reply": "2024-09-26T14:54:00.485402Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:58.911380Z", - "iopub.status.busy": "2024-09-06T19:39:58.911122Z", - "iopub.status.idle": "2024-09-06T19:39:58.914071Z", - "shell.execute_reply": "2024-09-06T19:39:58.913630Z" + "iopub.execute_input": "2024-09-26T14:54:00.488158Z", + "iopub.status.busy": "2024-09-26T14:54:00.487625Z", + "iopub.status.idle": "2024-09-26T14:54:00.490770Z", + "shell.execute_reply": "2024-09-26T14:54:00.490324Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:58.916066Z", - "iopub.status.busy": "2024-09-06T19:39:58.915883Z", - "iopub.status.idle": "2024-09-06T19:39:59.147435Z", - "shell.execute_reply": "2024-09-06T19:39:59.146903Z" + "iopub.execute_input": "2024-09-26T14:54:00.492476Z", + "iopub.status.busy": "2024-09-26T14:54:00.492170Z", + "iopub.status.idle": "2024-09-26T14:54:00.593026Z", + "shell.execute_reply": "2024-09-26T14:54:00.592503Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.149566Z", - "iopub.status.busy": "2024-09-06T19:39:59.149370Z", - "iopub.status.idle": "2024-09-06T19:39:59.331007Z", - "shell.execute_reply": "2024-09-06T19:39:59.330438Z" + "iopub.execute_input": "2024-09-26T14:54:00.595033Z", + "iopub.status.busy": "2024-09-26T14:54:00.594676Z", + "iopub.status.idle": "2024-09-26T14:54:00.781165Z", + "shell.execute_reply": "2024-09-26T14:54:00.780607Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.333486Z", - "iopub.status.busy": "2024-09-06T19:39:59.333040Z", - "iopub.status.idle": "2024-09-06T19:39:59.576590Z", - "shell.execute_reply": "2024-09-06T19:39:59.575968Z" + "iopub.execute_input": "2024-09-26T14:54:00.783347Z", + "iopub.status.busy": "2024-09-26T14:54:00.782969Z", + "iopub.status.idle": "2024-09-26T14:54:01.032458Z", + "shell.execute_reply": "2024-09-26T14:54:01.031929Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.578938Z", - "iopub.status.busy": "2024-09-06T19:39:59.578553Z", - "iopub.status.idle": "2024-09-06T19:39:59.582923Z", - "shell.execute_reply": "2024-09-06T19:39:59.582473Z" + "iopub.execute_input": "2024-09-26T14:54:01.034452Z", + "iopub.status.busy": "2024-09-26T14:54:01.034056Z", + "iopub.status.idle": "2024-09-26T14:54:01.038763Z", + "shell.execute_reply": "2024-09-26T14:54:01.038275Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.584759Z", - "iopub.status.busy": "2024-09-06T19:39:59.584580Z", - "iopub.status.idle": "2024-09-06T19:39:59.590790Z", - "shell.execute_reply": "2024-09-06T19:39:59.590351Z" + "iopub.execute_input": "2024-09-26T14:54:01.040507Z", + "iopub.status.busy": "2024-09-26T14:54:01.040163Z", + "iopub.status.idle": "2024-09-26T14:54:01.046197Z", + "shell.execute_reply": "2024-09-26T14:54:01.045737Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.592686Z", - "iopub.status.busy": "2024-09-06T19:39:59.592515Z", - "iopub.status.idle": "2024-09-06T19:39:59.595225Z", - "shell.execute_reply": "2024-09-06T19:39:59.594766Z" + "iopub.execute_input": "2024-09-26T14:54:01.048092Z", + "iopub.status.busy": "2024-09-26T14:54:01.047754Z", + "iopub.status.idle": "2024-09-26T14:54:01.050568Z", + "shell.execute_reply": "2024-09-26T14:54:01.050000Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:39:59.597032Z", - "iopub.status.busy": "2024-09-06T19:39:59.596865Z", - "iopub.status.idle": "2024-09-06T19:40:08.597697Z", - "shell.execute_reply": "2024-09-06T19:40:08.597120Z" + "iopub.execute_input": "2024-09-26T14:54:01.052488Z", + "iopub.status.busy": "2024-09-26T14:54:01.052092Z", + "iopub.status.idle": "2024-09-26T14:54:10.157589Z", + "shell.execute_reply": "2024-09-26T14:54:10.157001Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.600635Z", - "iopub.status.busy": "2024-09-06T19:40:08.599991Z", - "iopub.status.idle": "2024-09-06T19:40:08.607726Z", - "shell.execute_reply": "2024-09-06T19:40:08.607259Z" + "iopub.execute_input": "2024-09-26T14:54:10.160258Z", + "iopub.status.busy": "2024-09-26T14:54:10.159589Z", + "iopub.status.idle": "2024-09-26T14:54:10.167515Z", + "shell.execute_reply": "2024-09-26T14:54:10.167054Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.609816Z", - "iopub.status.busy": "2024-09-06T19:40:08.609470Z", - "iopub.status.idle": "2024-09-06T19:40:08.613036Z", - "shell.execute_reply": "2024-09-06T19:40:08.612542Z" + "iopub.execute_input": "2024-09-26T14:54:10.169285Z", + "iopub.status.busy": "2024-09-26T14:54:10.168935Z", + "iopub.status.idle": "2024-09-26T14:54:10.172611Z", + "shell.execute_reply": "2024-09-26T14:54:10.172168Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.615042Z", - "iopub.status.busy": "2024-09-06T19:40:08.614643Z", - "iopub.status.idle": "2024-09-06T19:40:08.618056Z", - "shell.execute_reply": "2024-09-06T19:40:08.617486Z" + "iopub.execute_input": "2024-09-26T14:54:10.174288Z", + "iopub.status.busy": "2024-09-26T14:54:10.173947Z", + "iopub.status.idle": "2024-09-26T14:54:10.177369Z", + "shell.execute_reply": "2024-09-26T14:54:10.176897Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.620104Z", - "iopub.status.busy": "2024-09-06T19:40:08.619791Z", - "iopub.status.idle": "2024-09-06T19:40:08.622907Z", - "shell.execute_reply": "2024-09-06T19:40:08.622416Z" + "iopub.execute_input": "2024-09-26T14:54:10.179183Z", + "iopub.status.busy": "2024-09-26T14:54:10.178849Z", + "iopub.status.idle": "2024-09-26T14:54:10.182081Z", + "shell.execute_reply": "2024-09-26T14:54:10.181652Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.624768Z", - "iopub.status.busy": "2024-09-06T19:40:08.624594Z", - "iopub.status.idle": "2024-09-06T19:40:08.632747Z", - "shell.execute_reply": "2024-09-06T19:40:08.632288Z" + "iopub.execute_input": "2024-09-26T14:54:10.183707Z", + "iopub.status.busy": "2024-09-26T14:54:10.183367Z", + "iopub.status.idle": "2024-09-26T14:54:10.191340Z", + "shell.execute_reply": "2024-09-26T14:54:10.190898Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.634564Z", - "iopub.status.busy": "2024-09-06T19:40:08.634392Z", - "iopub.status.idle": "2024-09-06T19:40:08.637116Z", - "shell.execute_reply": "2024-09-06T19:40:08.636642Z" + "iopub.execute_input": "2024-09-26T14:54:10.193003Z", + "iopub.status.busy": "2024-09-26T14:54:10.192665Z", + "iopub.status.idle": "2024-09-26T14:54:10.195213Z", + "shell.execute_reply": "2024-09-26T14:54:10.194766Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.639192Z", - "iopub.status.busy": "2024-09-06T19:40:08.638877Z", - "iopub.status.idle": "2024-09-06T19:40:08.766647Z", - "shell.execute_reply": "2024-09-06T19:40:08.765685Z" + "iopub.execute_input": "2024-09-26T14:54:10.196853Z", + "iopub.status.busy": "2024-09-26T14:54:10.196518Z", + "iopub.status.idle": "2024-09-26T14:54:10.322626Z", + "shell.execute_reply": "2024-09-26T14:54:10.322078Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.769173Z", - "iopub.status.busy": "2024-09-06T19:40:08.768972Z", - "iopub.status.idle": "2024-09-06T19:40:08.878186Z", - "shell.execute_reply": "2024-09-06T19:40:08.877593Z" + "iopub.execute_input": "2024-09-26T14:54:10.324771Z", + "iopub.status.busy": "2024-09-26T14:54:10.324359Z", + "iopub.status.idle": "2024-09-26T14:54:10.435194Z", + "shell.execute_reply": "2024-09-26T14:54:10.434642Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:08.880641Z", - "iopub.status.busy": "2024-09-06T19:40:08.880289Z", - "iopub.status.idle": "2024-09-06T19:40:09.386974Z", - "shell.execute_reply": "2024-09-06T19:40:09.386324Z" + "iopub.execute_input": "2024-09-26T14:54:10.437396Z", + "iopub.status.busy": "2024-09-26T14:54:10.436936Z", + "iopub.status.idle": "2024-09-26T14:54:10.943293Z", + "shell.execute_reply": "2024-09-26T14:54:10.942658Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.389675Z", - "iopub.status.busy": "2024-09-06T19:40:09.389308Z", - "iopub.status.idle": "2024-09-06T19:40:09.485553Z", - "shell.execute_reply": "2024-09-06T19:40:09.484996Z" + "iopub.execute_input": "2024-09-26T14:54:10.945562Z", + "iopub.status.busy": "2024-09-26T14:54:10.945188Z", + "iopub.status.idle": "2024-09-26T14:54:11.045547Z", + "shell.execute_reply": "2024-09-26T14:54:11.044913Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.487964Z", - "iopub.status.busy": "2024-09-06T19:40:09.487496Z", - "iopub.status.idle": "2024-09-06T19:40:09.496128Z", - "shell.execute_reply": "2024-09-06T19:40:09.495570Z" + "iopub.execute_input": "2024-09-26T14:54:11.047649Z", + "iopub.status.busy": "2024-09-26T14:54:11.047228Z", + "iopub.status.idle": "2024-09-26T14:54:11.055699Z", + "shell.execute_reply": "2024-09-26T14:54:11.055230Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.498303Z", - "iopub.status.busy": "2024-09-06T19:40:09.497989Z", - "iopub.status.idle": "2024-09-06T19:40:09.500756Z", - "shell.execute_reply": "2024-09-06T19:40:09.500274Z" + "iopub.execute_input": "2024-09-26T14:54:11.057456Z", + "iopub.status.busy": "2024-09-26T14:54:11.057092Z", + "iopub.status.idle": "2024-09-26T14:54:11.059706Z", + "shell.execute_reply": "2024-09-26T14:54:11.059257Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:09.502626Z", - "iopub.status.busy": "2024-09-06T19:40:09.502453Z", - "iopub.status.idle": "2024-09-06T19:40:15.134668Z", - "shell.execute_reply": "2024-09-06T19:40:15.134055Z" + "iopub.execute_input": "2024-09-26T14:54:11.061497Z", + "iopub.status.busy": "2024-09-26T14:54:11.061113Z", + "iopub.status.idle": "2024-09-26T14:54:16.702766Z", + "shell.execute_reply": "2024-09-26T14:54:16.702139Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:15.137003Z", - "iopub.status.busy": "2024-09-06T19:40:15.136794Z", - "iopub.status.idle": "2024-09-06T19:40:15.145626Z", - "shell.execute_reply": "2024-09-06T19:40:15.145149Z" + "iopub.execute_input": "2024-09-26T14:54:16.704653Z", + "iopub.status.busy": "2024-09-26T14:54:16.704460Z", + "iopub.status.idle": "2024-09-26T14:54:16.712980Z", + "shell.execute_reply": "2024-09-26T14:54:16.712530Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:15.147739Z", - "iopub.status.busy": "2024-09-06T19:40:15.147560Z", - "iopub.status.idle": "2024-09-06T19:40:15.212105Z", - "shell.execute_reply": "2024-09-06T19:40:15.211592Z" + "iopub.execute_input": "2024-09-26T14:54:16.714897Z", + "iopub.status.busy": "2024-09-26T14:54:16.714556Z", + "iopub.status.idle": "2024-09-26T14:54:16.786785Z", + "shell.execute_reply": "2024-09-26T14:54:16.786234Z" }, "nbsphinx": "hidden" }, @@ -1452,7 +1452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index 9c9811712..ff97a230b 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -804,13 +804,13 @@

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().

@@ -1200,7 +1200,7 @@

Get label quality scores -{"state": {"fefad91592514c8b93cde6a9aa658432": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "26c9d71cd2b144f5a62f2e547396cf9d": {"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": ""}}, "5050585031b24c079460a52a9a4fc488": {"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_fefad91592514c8b93cde6a9aa658432", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_26c9d71cd2b144f5a62f2e547396cf9d", "tabbable": null, "tooltip": null, "value": 30.0}}, "08c7a2f2c6804a7da25a3555d45832fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5428bca92792410db3731a76852725a2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "42d03e2415284486b25cf67ccd387444": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_08c7a2f2c6804a7da25a3555d45832fe", "placeholder": "\u200b", "style": "IPY_MODEL_5428bca92792410db3731a76852725a2", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "474187191bb2423bbeaab8075807fc8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5aa47fe7e6cf4464bcbe167e6d3ba68a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b1848abf52f742ed9f7657ba08af06f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_474187191bb2423bbeaab8075807fc8d", "placeholder": "\u200b", "style": "IPY_MODEL_5aa47fe7e6cf4464bcbe167e6d3ba68a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007787.55it/s]"}}, "d73b0ac161c9411fb176d09cfe007d5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "00ec60662f03441f8733d768775a0ed1": {"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_42d03e2415284486b25cf67ccd387444", "IPY_MODEL_5050585031b24c079460a52a9a4fc488", "IPY_MODEL_b1848abf52f742ed9f7657ba08af06f7"], "layout": "IPY_MODEL_d73b0ac161c9411fb176d09cfe007d5d", "tabbable": null, "tooltip": null}}, "71cc03f01ffb487095fef61fe310cb72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "428890bcca0c4c398b4c85e7b197ef23": {"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": ""}}, "5e211e7a482d4ffc95757eed7f7aa9cc": {"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_71cc03f01ffb487095fef61fe310cb72", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_428890bcca0c4c398b4c85e7b197ef23", "tabbable": null, "tooltip": null, "value": 30.0}}, "733932bb0ae3401390e27945e01e9afa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "72aa2b7d62f44bfba1fef33687cd2d9c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ac71e20e794944a5ad10d81bd3802d6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_733932bb0ae3401390e27945e01e9afa", "placeholder": "\u200b", "style": "IPY_MODEL_72aa2b7d62f44bfba1fef33687cd2d9c", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "8b4141a6045142c1b9ba131103d924f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "62e07c87b8f14d10ae3081dc89c264cb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "56086d38b6e24dd381b3d2d8adfc7dee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8b4141a6045142c1b9ba131103d924f0", "placeholder": "\u200b", "style": "IPY_MODEL_62e07c87b8f14d10ae3081dc89c264cb", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:25<00:00,\u2007\u20071.19it/s]"}}, "71d9c9ff1e934321985ce73f6d70432d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "af401850ebaa408dae00a90bb34bc54a": {"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_ac71e20e794944a5ad10d81bd3802d6a", "IPY_MODEL_5e211e7a482d4ffc95757eed7f7aa9cc", "IPY_MODEL_56086d38b6e24dd381b3d2d8adfc7dee"], "layout": "IPY_MODEL_71d9c9ff1e934321985ce73f6d70432d", "tabbable": null, "tooltip": null}}, "0555e6f1fc524e749446c0929d265eab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b4ff48b5ef42475cb8d931380feef05a": {"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": ""}}, "8c44f5cb10834552b9f054ccff28de8f": {"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_0555e6f1fc524e749446c0929d265eab", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b4ff48b5ef42475cb8d931380feef05a", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "31810f3656744673bb829bd7c19b4796": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "71684d8531234f3d9d16e15f5e2a1318": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e7a051930ecf4f8da5a7114fa550bc7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_31810f3656744673bb829bd7c19b4796", "placeholder": "\u200b", "style": "IPY_MODEL_71684d8531234f3d9d16e15f5e2a1318", "tabbable": null, "tooltip": null, "value": "100%"}}, "23f68f6bcc9f4247ac306e707ae76a3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0f26c903e03a409eb8eb23a06ad068a1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "abb55722ee8a4e9383f54ba9776bfb21": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_23f68f6bcc9f4247ac306e707ae76a3e", "placeholder": "\u200b", "style": "IPY_MODEL_0f26c903e03a409eb8eb23a06ad068a1", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:33<00:00,\u2007147431.29it/s]"}}, "44a941086c164d5bb775c41c7d4ac57f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a8ef1d6ee6da4d52bd3aa4ef30d9915f": {"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_e7a051930ecf4f8da5a7114fa550bc7c", "IPY_MODEL_8c44f5cb10834552b9f054ccff28de8f", "IPY_MODEL_abb55722ee8a4e9383f54ba9776bfb21"], "layout": "IPY_MODEL_44a941086c164d5bb775c41c7d4ac57f", "tabbable": null, "tooltip": null}}, "d8f70224ecee42f48ecf14d646040c54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e468d38bc9454ebf87117d355645f3f1": {"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": ""}}, "90fef083c08c4c3c927458dfb8b00fe9": {"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_d8f70224ecee42f48ecf14d646040c54", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e468d38bc9454ebf87117d355645f3f1", "tabbable": null, "tooltip": null, "value": 30.0}}, "fa2dd8d15728476eac598aeb95576e3b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a247c69930644302aed767d71b7ec676": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6901169080b04ad499942dc391b9b336": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fa2dd8d15728476eac598aeb95576e3b", "placeholder": "\u200b", "style": "IPY_MODEL_a247c69930644302aed767d71b7ec676", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "7b66b5652e59476aab6385c55f338eaf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c70c5f7b514a4120b47fe4694b8aa561": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "219cc478643a4ee5ac3bd50beeb53306": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7b66b5652e59476aab6385c55f338eaf", "placeholder": "\u200b", "style": "IPY_MODEL_c70c5f7b514a4120b47fe4694b8aa561", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.35it/s]"}}, "571062df41e24ec2a51ede636c1c40ae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "33547ea19ce34215b8f9bbd75c870924": {"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_6901169080b04ad499942dc391b9b336", "IPY_MODEL_90fef083c08c4c3c927458dfb8b00fe9", "IPY_MODEL_219cc478643a4ee5ac3bd50beeb53306"], "layout": "IPY_MODEL_571062df41e24ec2a51ede636c1c40ae", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"d5f572bcf9e34ff5b3f799cfc3b2c03c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc418d04bfd44dc999d29a7cfbaf1bf5": {"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": ""}}, "03edd2e8077d415a86a42428227957c1": {"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_d5f572bcf9e34ff5b3f799cfc3b2c03c", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", "tabbable": null, "tooltip": null, "value": 30.0}}, "a6ce96bd4a1f4a83b1164ac6cbe3d02f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "38f90d531db8409db73b7389ee4986c2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "5d4af35c70b14d9b95542e9fbacf5ee2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", "placeholder": "\u200b", "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "aad869b6d41d459097999efed9f5aabb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7211d82a11904799ba5182ef4f7e1762": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d408f59a6c4642dbacacf8536dd5bb86": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", "placeholder": "\u200b", "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007761.55it/s]"}}, "b25023ee46574f0987d4401430bdbe95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0d3c194b71ae41699ecaf593bb466ee6": {"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_5d4af35c70b14d9b95542e9fbacf5ee2", "IPY_MODEL_03edd2e8077d415a86a42428227957c1", "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86"], "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", "tabbable": null, "tooltip": null}}, "496448b9dfc748d7b07ed9a700cc1ab7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c19fb70e50ef4b3aa215c397be2fa0ed": {"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": ""}}, "477c45c2e60a4cc7bc955c274f038c75": {"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_496448b9dfc748d7b07ed9a700cc1ab7", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", "tabbable": null, "tooltip": null, "value": 30.0}}, "cbe2b73a42764a2aacaeaee0b9c612b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d26696e0cc9f4eef935b52e6f5301e41": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "21f930a5e16b44e6896cab16aadf76b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", "placeholder": "\u200b", "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "ffca702bd3444f1690f1f5f85493ca09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "008823ad1c554e5fa5b1815e6e7eee3a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "15a01925ca5e45e5bb086a7b185ac53c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", "placeholder": "\u200b", "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:25<00:00,\u2007\u20071.22it/s]"}}, "6fc3fa5fef38489287ed8d9f7c6e1c3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f246aefc67174f658fc6990471fd838b": {"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_21f930a5e16b44e6896cab16aadf76b0", "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c"], "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", "tabbable": null, "tooltip": null}}, "3539b50ce0e843448d49322ce25b2b2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5ab8498d427444c6b4d07bf8d5bc6157": {"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": ""}}, "c126cab8ab9c4826849b4c390465afaf": {"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_3539b50ce0e843448d49322ce25b2b2e", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "6c7533cc89b74b90bdcf06dda9d4297f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aedea8fe506b40c1933ac0b06c3dc5c7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2e397451daaa420aac06b58d115ddb89": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", "placeholder": "\u200b", "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", "tabbable": null, "tooltip": null, "value": "100%"}}, "11d7b29b91e94ee382b5c3abbb5da356": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2cada3550c7444318e24a19d4c5bac92": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1b8ddda746534779bbbb5fd4b8f8df0b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", "placeholder": "\u200b", "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007153968.10it/s]"}}, "60825090590c4420817f531a12ba0cb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b6f8c999233c44e6b60c123e18607ca1": {"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_2e397451daaa420aac06b58d115ddb89", "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b"], "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", "tabbable": null, "tooltip": null}}, "414724ec89444e8ebc1105e3c21216d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0780c5558e44bdf9cd38943fbc6879f": {"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": ""}}, "db3abba05009401583103fd3bfc35643": {"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_414724ec89444e8ebc1105e3c21216d3", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", "tabbable": null, "tooltip": null, "value": 30.0}}, "038939be2791404a8d8b3535498c5720": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e7222a4d37404d41a68f2bf782915ef2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e15e2d1b74894e47b98ed243861d83d8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", "placeholder": "\u200b", "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "aeabc766c99c4e2b8edffb93d948620a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cbf3bc2871f144bda8f96df51315cc6a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4db5293fd3e94b6eb261d17cfdd19337": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", "placeholder": "\u200b", "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200720.31it/s]"}}, "c41365fd01984997be6e7450cfa7d4d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5444a2dc1c4c403ab396248114105df7": {"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_e15e2d1b74894e47b98ed243861d83d8", "IPY_MODEL_db3abba05009401583103fd3bfc35643", "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337"], "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 3d1ba85ed..6779478cb 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:18.378801Z", - "iopub.status.busy": "2024-09-06T19:40:18.378438Z", - "iopub.status.idle": "2024-09-06T19:40:21.013953Z", - "shell.execute_reply": "2024-09-06T19:40:21.013191Z" + "iopub.execute_input": "2024-09-26T14:54:20.095591Z", + "iopub.status.busy": "2024-09-26T14:54:20.095416Z", + "iopub.status.idle": "2024-09-26T14:54:23.030061Z", + "shell.execute_reply": "2024-09-26T14:54:23.029312Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:40:21.016497Z", - "iopub.status.busy": "2024-09-06T19:40:21.016297Z", - "iopub.status.idle": "2024-09-06T19:41:26.205588Z", - "shell.execute_reply": "2024-09-06T19:41:26.204905Z" + "iopub.execute_input": "2024-09-26T14:54:23.032402Z", + "iopub.status.busy": "2024-09-26T14:54:23.032024Z", + "iopub.status.idle": "2024-09-26T14:55:28.921952Z", + "shell.execute_reply": "2024-09-26T14:55:28.921155Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:26.208261Z", - "iopub.status.busy": "2024-09-06T19:41:26.207954Z", - "iopub.status.idle": "2024-09-06T19:41:27.363762Z", - "shell.execute_reply": "2024-09-06T19:41:27.363213Z" + "iopub.execute_input": "2024-09-26T14:55:28.924172Z", + "iopub.status.busy": "2024-09-26T14:55:28.923971Z", + "iopub.status.idle": "2024-09-26T14:55:30.137143Z", + "shell.execute_reply": "2024-09-26T14:55:30.136538Z" }, "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\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": "2024-09-06T19:41:27.366273Z", - "iopub.status.busy": "2024-09-06T19:41:27.365850Z", - "iopub.status.idle": "2024-09-06T19:41:27.369197Z", - "shell.execute_reply": "2024-09-06T19:41:27.368626Z" + "iopub.execute_input": "2024-09-26T14:55:30.139396Z", + "iopub.status.busy": "2024-09-26T14:55:30.139106Z", + "iopub.status.idle": "2024-09-26T14:55:30.142481Z", + "shell.execute_reply": "2024-09-26T14:55:30.141914Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.371272Z", - "iopub.status.busy": "2024-09-06T19:41:27.370943Z", - "iopub.status.idle": "2024-09-06T19:41:27.374872Z", - "shell.execute_reply": "2024-09-06T19:41:27.374336Z" + "iopub.execute_input": "2024-09-26T14:55:30.144228Z", + "iopub.status.busy": "2024-09-26T14:55:30.144050Z", + "iopub.status.idle": "2024-09-26T14:55:30.147926Z", + "shell.execute_reply": "2024-09-26T14:55:30.147419Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.377058Z", - "iopub.status.busy": "2024-09-06T19:41:27.376708Z", - "iopub.status.idle": "2024-09-06T19:41:27.380273Z", - "shell.execute_reply": "2024-09-06T19:41:27.379824Z" + "iopub.execute_input": "2024-09-26T14:55:30.149873Z", + "iopub.status.busy": "2024-09-26T14:55:30.149499Z", + "iopub.status.idle": "2024-09-26T14:55:30.153440Z", + "shell.execute_reply": "2024-09-26T14:55:30.152904Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.382286Z", - "iopub.status.busy": "2024-09-06T19:41:27.381955Z", - "iopub.status.idle": "2024-09-06T19:41:27.384835Z", - "shell.execute_reply": "2024-09-06T19:41:27.384366Z" + "iopub.execute_input": "2024-09-26T14:55:30.155269Z", + "iopub.status.busy": "2024-09-26T14:55:30.154919Z", + "iopub.status.idle": "2024-09-26T14:55:30.158026Z", + "shell.execute_reply": "2024-09-26T14:55:30.157441Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:41:27.386838Z", - "iopub.status.busy": "2024-09-06T19:41:27.386506Z", - "iopub.status.idle": "2024-09-06T19:42:04.890778Z", - "shell.execute_reply": "2024-09-06T19:42:04.890135Z" + "iopub.execute_input": "2024-09-26T14:55:30.159991Z", + "iopub.status.busy": "2024-09-26T14:55:30.159527Z", + "iopub.status.idle": "2024-09-26T14:56:07.853263Z", + "shell.execute_reply": "2024-09-26T14:56:07.852683Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00ec60662f03441f8733d768775a0ed1", + "model_id": "0d3c194b71ae41699ecaf593bb466ee6", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af401850ebaa408dae00a90bb34bc54a", + "model_id": "f246aefc67174f658fc6990471fd838b", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:04.893407Z", - "iopub.status.busy": "2024-09-06T19:42:04.893064Z", - "iopub.status.idle": "2024-09-06T19:42:05.569760Z", - "shell.execute_reply": "2024-09-06T19:42:05.569193Z" + "iopub.execute_input": "2024-09-26T14:56:07.855727Z", + "iopub.status.busy": "2024-09-26T14:56:07.855280Z", + "iopub.status.idle": "2024-09-26T14:56:08.539218Z", + "shell.execute_reply": "2024-09-26T14:56:08.538732Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:05.572221Z", - "iopub.status.busy": "2024-09-06T19:42:05.571699Z", - "iopub.status.idle": "2024-09-06T19:42:08.487750Z", - "shell.execute_reply": "2024-09-06T19:42:08.487151Z" + "iopub.execute_input": "2024-09-26T14:56:08.541245Z", + "iopub.status.busy": "2024-09-26T14:56:08.540794Z", + "iopub.status.idle": "2024-09-26T14:56:11.382690Z", + "shell.execute_reply": "2024-09-26T14:56:11.382214Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:08.490015Z", - "iopub.status.busy": "2024-09-06T19:42:08.489812Z", - "iopub.status.idle": "2024-09-06T19:42:42.122207Z", - "shell.execute_reply": "2024-09-06T19:42:42.121639Z" + "iopub.execute_input": "2024-09-26T14:56:11.384692Z", + "iopub.status.busy": "2024-09-26T14:56:11.384341Z", + "iopub.status.idle": "2024-09-26T14:56:43.908330Z", + "shell.execute_reply": "2024-09-26T14:56:43.907746Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8ef1d6ee6da4d52bd3aa4ef30d9915f", + "model_id": "b6f8c999233c44e6b60c123e18607ca1", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:42.124501Z", - "iopub.status.busy": "2024-09-06T19:42:42.124158Z", - "iopub.status.idle": "2024-09-06T19:42:57.234866Z", - "shell.execute_reply": "2024-09-06T19:42:57.234293Z" + "iopub.execute_input": "2024-09-26T14:56:43.910328Z", + "iopub.status.busy": "2024-09-26T14:56:43.909998Z", + "iopub.status.idle": "2024-09-26T14:56:59.179852Z", + "shell.execute_reply": "2024-09-26T14:56:59.179195Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:42:57.237390Z", - "iopub.status.busy": "2024-09-06T19:42:57.237016Z", - "iopub.status.idle": "2024-09-06T19:43:00.971913Z", - "shell.execute_reply": "2024-09-06T19:43:00.971312Z" + "iopub.execute_input": "2024-09-26T14:56:59.182094Z", + "iopub.status.busy": "2024-09-26T14:56:59.181790Z", + "iopub.status.idle": "2024-09-26T14:57:03.058054Z", + "shell.execute_reply": "2024-09-26T14:57:03.057557Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:00.974009Z", - "iopub.status.busy": "2024-09-06T19:43:00.973827Z", - "iopub.status.idle": "2024-09-06T19:43:02.404764Z", - "shell.execute_reply": "2024-09-06T19:43:02.404239Z" + "iopub.execute_input": "2024-09-26T14:57:03.059722Z", + "iopub.status.busy": "2024-09-26T14:57:03.059541Z", + "iopub.status.idle": "2024-09-26T14:57:04.551144Z", + "shell.execute_reply": "2024-09-26T14:57:04.550565Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33547ea19ce34215b8f9bbd75c870924", + "model_id": "5444a2dc1c4c403ab396248114105df7", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:02.407222Z", - "iopub.status.busy": "2024-09-06T19:43:02.406914Z", - "iopub.status.idle": "2024-09-06T19:43:02.435740Z", - "shell.execute_reply": "2024-09-06T19:43:02.435223Z" + "iopub.execute_input": "2024-09-26T14:57:04.553436Z", + "iopub.status.busy": "2024-09-26T14:57:04.552948Z", + "iopub.status.idle": "2024-09-26T14:57:04.584746Z", + "shell.execute_reply": "2024-09-26T14:57:04.584077Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:02.438408Z", - "iopub.status.busy": "2024-09-06T19:43:02.438030Z", - "iopub.status.idle": "2024-09-06T19:43:08.523002Z", - "shell.execute_reply": "2024-09-06T19:43:08.522439Z" + "iopub.execute_input": "2024-09-26T14:57:04.587085Z", + "iopub.status.busy": "2024-09-26T14:57:04.586688Z", + "iopub.status.idle": "2024-09-26T14:57:10.717190Z", + "shell.execute_reply": "2024-09-26T14:57:10.716706Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:08.525189Z", - "iopub.status.busy": "2024-09-06T19:43:08.524868Z", - "iopub.status.idle": "2024-09-06T19:43:08.580916Z", - "shell.execute_reply": "2024-09-06T19:43:08.580242Z" + "iopub.execute_input": "2024-09-26T14:57:10.719179Z", + "iopub.status.busy": "2024-09-26T14:57:10.718830Z", + "iopub.status.idle": "2024-09-26T14:57:10.774462Z", + "shell.execute_reply": "2024-09-26T14:57:10.773892Z" }, "nbsphinx": "hidden" }, @@ -1033,36 +1033,30 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "00ec60662f03441f8733d768775a0ed1": { + "008823ad1c554e5fa5b1815e6e7eee3a": { "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_42d03e2415284486b25cf67ccd387444", - "IPY_MODEL_5050585031b24c079460a52a9a4fc488", - "IPY_MODEL_b1848abf52f742ed9f7657ba08af06f7" - ], - "layout": "IPY_MODEL_d73b0ac161c9411fb176d09cfe007d5d", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "0555e6f1fc524e749446c0929d265eab": { + "038939be2791404a8d8b3535498c5720": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1115,7 +1109,57 @@ "width": null } }, - "08c7a2f2c6804a7da25a3555d45832fe": { + "03edd2e8077d415a86a42428227957c1": { + "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_d5f572bcf9e34ff5b3f799cfc3b2c03c", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fc418d04bfd44dc999d29a7cfbaf1bf5", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "0d3c194b71ae41699ecaf593bb466ee6": { + "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_5d4af35c70b14d9b95542e9fbacf5ee2", + "IPY_MODEL_03edd2e8077d415a86a42428227957c1", + "IPY_MODEL_d408f59a6c4642dbacacf8536dd5bb86" + ], + "layout": "IPY_MODEL_b25023ee46574f0987d4401430bdbe95", + "tabbable": null, + "tooltip": null + } + }, + "11d7b29b91e94ee382b5c3abbb5da356": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1168,7 +1212,76 @@ "width": null } }, - "0f26c903e03a409eb8eb23a06ad068a1": { + "15a01925ca5e45e5bb086a7b185ac53c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ffca702bd3444f1690f1f5f85493ca09", + "placeholder": "​", + "style": "IPY_MODEL_008823ad1c554e5fa5b1815e6e7eee3a", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:25<00:00,  1.22it/s]" + } + }, + "1b8ddda746534779bbbb5fd4b8f8df0b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_11d7b29b91e94ee382b5c3abbb5da356", + "placeholder": "​", + "style": "IPY_MODEL_2cada3550c7444318e24a19d4c5bac92", + "tabbable": null, + "tooltip": null, + "value": " 4997683/4997683 [00:32<00:00, 153968.10it/s]" + } + }, + "21f930a5e16b44e6896cab16aadf76b0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cbe2b73a42764a2aacaeaee0b9c612b7", + "placeholder": "​", + "style": "IPY_MODEL_d26696e0cc9f4eef935b52e6f5301e41", + "tabbable": null, + "tooltip": null, + "value": "number of examples processed for checking labels: 100%" + } + }, + "2cada3550c7444318e24a19d4c5bac92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1186,7 +1299,7 @@ "text_color": null } }, - "219cc478643a4ee5ac3bd50beeb53306": { + "2e397451daaa420aac06b58d115ddb89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1201,15 +1314,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7b66b5652e59476aab6385c55f338eaf", + "layout": "IPY_MODEL_6c7533cc89b74b90bdcf06dda9d4297f", "placeholder": "​", - "style": "IPY_MODEL_c70c5f7b514a4120b47fe4694b8aa561", + "style": "IPY_MODEL_aedea8fe506b40c1933ac0b06c3dc5c7", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.35it/s]" + "value": "100%" } }, - "23f68f6bcc9f4247ac306e707ae76a3e": { + "3539b50ce0e843448d49322ce25b2b2e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,23 +1375,104 @@ "width": null } }, - "26c9d71cd2b144f5a62f2e547396cf9d": { + "38f90d531db8409db73b7389ee4986c2": { "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 + } + }, + "414724ec89444e8ebc1105e3c21216d3": { + "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 + } + }, + "477c45c2e60a4cc7bc955c274f038c75": { + "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_496448b9dfc748d7b07ed9a700cc1ab7", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c19fb70e50ef4b3aa215c397be2fa0ed", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "31810f3656744673bb829bd7c19b4796": { + "496448b9dfc748d7b07ed9a700cc1ab7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1331,7 +1525,30 @@ "width": null } }, - "33547ea19ce34215b8f9bbd75c870924": { + "4db5293fd3e94b6eb261d17cfdd19337": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_aeabc766c99c4e2b8edffb93d948620a", + "placeholder": "​", + "style": "IPY_MODEL_cbf3bc2871f144bda8f96df51315cc6a", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 20.31it/s]" + } + }, + "5444a2dc1c4c403ab396248114105df7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1346,16 +1563,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_6901169080b04ad499942dc391b9b336", - "IPY_MODEL_90fef083c08c4c3c927458dfb8b00fe9", - "IPY_MODEL_219cc478643a4ee5ac3bd50beeb53306" + "IPY_MODEL_e15e2d1b74894e47b98ed243861d83d8", + "IPY_MODEL_db3abba05009401583103fd3bfc35643", + "IPY_MODEL_4db5293fd3e94b6eb261d17cfdd19337" ], - "layout": "IPY_MODEL_571062df41e24ec2a51ede636c1c40ae", + "layout": "IPY_MODEL_c41365fd01984997be6e7450cfa7d4d5", "tabbable": null, "tooltip": null } }, - "428890bcca0c4c398b4c85e7b197ef23": { + "5ab8498d427444c6b4d07bf8d5bc6157": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1371,7 +1588,7 @@ "description_width": "" } }, - "42d03e2415284486b25cf67ccd387444": { + "5d4af35c70b14d9b95542e9fbacf5ee2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1386,15 +1603,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_08c7a2f2c6804a7da25a3555d45832fe", + "layout": "IPY_MODEL_a6ce96bd4a1f4a83b1164ac6cbe3d02f", "placeholder": "​", - "style": "IPY_MODEL_5428bca92792410db3731a76852725a2", + "style": "IPY_MODEL_38f90d531db8409db73b7389ee4986c2", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: 100%" } }, - "44a941086c164d5bb775c41c7d4ac57f": { + "60825090590c4420817f531a12ba0cb9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1447,7 +1664,7 @@ "width": null } }, - "474187191bb2423bbeaab8075807fc8d": { + "6c7533cc89b74b90bdcf06dda9d4297f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,81 +1717,14 @@ "width": null } }, - "5050585031b24c079460a52a9a4fc488": { - "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_fefad91592514c8b93cde6a9aa658432", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_26c9d71cd2b144f5a62f2e547396cf9d", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "5428bca92792410db3731a76852725a2": { - "model_module": "@jupyter-widgets/controls", + "6fc3fa5fef38489287ed8d9f7c6e1c3e": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "56086d38b6e24dd381b3d2d8adfc7dee": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8b4141a6045142c1b9ba131103d924f0", - "placeholder": "​", - "style": "IPY_MODEL_62e07c87b8f14d10ae3081dc89c264cb", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:25<00:00,  1.19it/s]" - } - }, - "571062df41e24ec2a51ede636c1c40ae": { - "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", @@ -1620,7 +1770,7 @@ "width": null } }, - "5aa47fe7e6cf4464bcbe167e6d3ba68a": { + "7211d82a11904799ba5182ef4f7e1762": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1638,92 +1788,7 @@ "text_color": null } }, - "5e211e7a482d4ffc95757eed7f7aa9cc": { - "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_71cc03f01ffb487095fef61fe310cb72", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_428890bcca0c4c398b4c85e7b197ef23", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "62e07c87b8f14d10ae3081dc89c264cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6901169080b04ad499942dc391b9b336": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fa2dd8d15728476eac598aeb95576e3b", - "placeholder": "​", - "style": "IPY_MODEL_a247c69930644302aed767d71b7ec676", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "71684d8531234f3d9d16e15f5e2a1318": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "71cc03f01ffb487095fef61fe310cb72": { + "a6ce96bd4a1f4a83b1164ac6cbe3d02f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1776,7 +1841,7 @@ "width": null } }, - "71d9c9ff1e934321985ce73f6d70432d": { + "aad869b6d41d459097999efed9f5aabb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1829,25 +1894,7 @@ "width": null } }, - "72aa2b7d62f44bfba1fef33687cd2d9c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "733932bb0ae3401390e27945e01e9afa": { + "aeabc766c99c4e2b8edffb93d948620a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1900,60 +1947,25 @@ "width": null } }, - "7b66b5652e59476aab6385c55f338eaf": { - "model_module": "@jupyter-widgets/base", + "aedea8fe506b40c1933ac0b06c3dc5c7": { + "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 } }, - "8b4141a6045142c1b9ba131103d924f0": { + "b25023ee46574f0987d4401430bdbe95": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2006,77 +2018,7 @@ "width": null } }, - "8c44f5cb10834552b9f054ccff28de8f": { - "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_0555e6f1fc524e749446c0929d265eab", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b4ff48b5ef42475cb8d931380feef05a", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 - } - }, - "90fef083c08c4c3c927458dfb8b00fe9": { - "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_d8f70224ecee42f48ecf14d646040c54", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e468d38bc9454ebf87117d355645f3f1", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "a247c69930644302aed767d71b7ec676": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a8ef1d6ee6da4d52bd3aa4ef30d9915f": { + "b6f8c999233c44e6b60c123e18607ca1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2091,109 +2033,58 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e7a051930ecf4f8da5a7114fa550bc7c", - "IPY_MODEL_8c44f5cb10834552b9f054ccff28de8f", - "IPY_MODEL_abb55722ee8a4e9383f54ba9776bfb21" + "IPY_MODEL_2e397451daaa420aac06b58d115ddb89", + "IPY_MODEL_c126cab8ab9c4826849b4c390465afaf", + "IPY_MODEL_1b8ddda746534779bbbb5fd4b8f8df0b" ], - "layout": "IPY_MODEL_44a941086c164d5bb775c41c7d4ac57f", + "layout": "IPY_MODEL_60825090590c4420817f531a12ba0cb9", "tabbable": null, "tooltip": null } }, - "abb55722ee8a4e9383f54ba9776bfb21": { + "c0780c5558e44bdf9cd38943fbc6879f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_23f68f6bcc9f4247ac306e707ae76a3e", - "placeholder": "​", - "style": "IPY_MODEL_0f26c903e03a409eb8eb23a06ad068a1", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:33<00:00, 147431.29it/s]" - } - }, - "ac71e20e794944a5ad10d81bd3802d6a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_733932bb0ae3401390e27945e01e9afa", - "placeholder": "​", - "style": "IPY_MODEL_72aa2b7d62f44bfba1fef33687cd2d9c", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: 100%" - } - }, - "af401850ebaa408dae00a90bb34bc54a": { - "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_ac71e20e794944a5ad10d81bd3802d6a", - "IPY_MODEL_5e211e7a482d4ffc95757eed7f7aa9cc", - "IPY_MODEL_56086d38b6e24dd381b3d2d8adfc7dee" - ], - "layout": "IPY_MODEL_71d9c9ff1e934321985ce73f6d70432d", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "b1848abf52f742ed9f7657ba08af06f7": { + "c126cab8ab9c4826849b4c390465afaf": { "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_474187191bb2423bbeaab8075807fc8d", - "placeholder": "​", - "style": "IPY_MODEL_5aa47fe7e6cf4464bcbe167e6d3ba68a", + "layout": "IPY_MODEL_3539b50ce0e843448d49322ce25b2b2e", + "max": 4997683.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5ab8498d427444c6b4d07bf8d5bc6157", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:00<00:00, 787.55it/s]" + "value": 4997683.0 } }, - "b4ff48b5ef42475cb8d931380feef05a": { + "c19fb70e50ef4b3aa215c397be2fa0ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2209,25 +2100,7 @@ "description_width": "" } }, - "c70c5f7b514a4120b47fe4694b8aa561": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d73b0ac161c9411fb176d09cfe007d5d": { + "c41365fd01984997be6e7450cfa7d4d5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2280,7 +2153,7 @@ "width": null } }, - "d8f70224ecee42f48ecf14d646040c54": { + "cbe2b73a42764a2aacaeaee0b9c612b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2333,23 +2206,43 @@ "width": null } }, - "e468d38bc9454ebf87117d355645f3f1": { + "cbf3bc2871f144bda8f96df51315cc6a": { "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 } }, - "e7a051930ecf4f8da5a7114fa550bc7c": { + "d26696e0cc9f4eef935b52e6f5301e41": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d408f59a6c4642dbacacf8536dd5bb86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2364,15 +2257,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_31810f3656744673bb829bd7c19b4796", + "layout": "IPY_MODEL_aad869b6d41d459097999efed9f5aabb", "placeholder": "​", - "style": "IPY_MODEL_71684d8531234f3d9d16e15f5e2a1318", + "style": "IPY_MODEL_7211d82a11904799ba5182ef4f7e1762", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 30/30 [00:00<00:00, 761.55it/s]" } }, - "fa2dd8d15728476eac598aeb95576e3b": { + "d5f572bcf9e34ff5b3f799cfc3b2c03c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2425,7 +2318,114 @@ "width": null } }, - "fefad91592514c8b93cde6a9aa658432": { + "db3abba05009401583103fd3bfc35643": { + "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_414724ec89444e8ebc1105e3c21216d3", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c0780c5558e44bdf9cd38943fbc6879f", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "e15e2d1b74894e47b98ed243861d83d8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_038939be2791404a8d8b3535498c5720", + "placeholder": "​", + "style": "IPY_MODEL_e7222a4d37404d41a68f2bf782915ef2", + "tabbable": null, + "tooltip": null, + "value": "images processed using softmin: 100%" + } + }, + "e7222a4d37404d41a68f2bf782915ef2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "f246aefc67174f658fc6990471fd838b": { + "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_21f930a5e16b44e6896cab16aadf76b0", + "IPY_MODEL_477c45c2e60a4cc7bc955c274f038c75", + "IPY_MODEL_15a01925ca5e45e5bb086a7b185ac53c" + ], + "layout": "IPY_MODEL_6fc3fa5fef38489287ed8d9f7c6e1c3e", + "tabbable": null, + "tooltip": null + } + }, + "fc418d04bfd44dc999d29a7cfbaf1bf5": { + "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": "" + } + }, + "ffca702bd3444f1690f1f5f85493ca09": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index bad7e549d..413046fc5 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -714,16 +714,16 @@

1. Install required dependencies and download data

diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index c988c12c2..9d0e0764f 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:11.117353Z", - "iopub.status.busy": "2024-09-06T19:43:11.117178Z", - "iopub.status.idle": "2024-09-06T19:43:13.210573Z", - "shell.execute_reply": "2024-09-06T19:43:13.209958Z" + "iopub.execute_input": "2024-09-26T14:57:13.331707Z", + "iopub.status.busy": "2024-09-26T14:57:13.331541Z", + "iopub.status.idle": "2024-09-26T14:57:15.936866Z", + "shell.execute_reply": "2024-09-26T14:57:15.936192Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:43:11-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-09-26 14:57:13-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.249.167, 2400:52e0:1a01::907:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.249.167|:443... connected.\r\n", + "185.93.1.243, 2400:52e0:1a00::940:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.243|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -118,7 +118,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-09-06 19:43:11 (7.82 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-09-26 14:57:13 (7.67 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -127,33 +127,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Archive: conll2003.zip\r\n", - " inflating: data/metadata \r\n", - " inflating: data/test.txt \r\n", - " inflating: data/train.txt \r\n", - " inflating: data/valid.txt \r\n" + "Archive: conll2003.zip\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-09-06 19:43:11-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.201.17, 52.217.193.233, 52.217.81.84, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.201.17|:443... " + " inflating: data/metadata \r\n", + " inflating: data/test.txt \r\n", + " inflating: data/train.txt \r\n", + " inflating: data/valid.txt \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "connected.\r\n" + "--2024-09-26 14:57:14-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.27.119, 52.217.207.97, 52.217.171.81, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.27.119|:443... " ] }, { "name": "stdout", "output_type": "stream", "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,7 +174,31 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 0%[ ] 142.53K 668KB/s " + "pred_probs.npz 2%[ ] 482.32K 2.17MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 7%[> ] 1.23M 2.84MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 14%[=> ] 2.42M 3.72MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 26%[====> ] 4.26M 4.90MB/s " ] }, { @@ -182,7 +206,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 8%[> ] 1.35M 3.16MB/s " + "pred_probs.npz 43%[=======> ] 7.12M 6.54MB/s " ] }, { @@ -190,7 +214,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 50%[=========> ] 8.28M 12.9MB/s " + "pred_probs.npz 71%[=============> ] 11.56M 8.85MB/s " ] }, { @@ -198,9 +222,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 20.4MB/s in 0.8s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 11.2MB/s in 1.5s \r\n", "\r\n", - "2024-09-06 19:43:13 (20.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-09-26 14:57:15 (11.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -217,10 +241,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:13.213109Z", - "iopub.status.busy": "2024-09-06T19:43:13.212725Z", - "iopub.status.idle": "2024-09-06T19:43:14.513752Z", - "shell.execute_reply": "2024-09-06T19:43:14.513226Z" + "iopub.execute_input": "2024-09-26T14:57:15.939149Z", + "iopub.status.busy": "2024-09-26T14:57:15.938782Z", + "iopub.status.idle": "2024-09-26T14:57:17.187528Z", + "shell.execute_reply": "2024-09-26T14:57:17.186884Z" }, "nbsphinx": "hidden" }, @@ -231,7 +255,7 @@ "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@9c563ed5c55574f3f6fa5ce0532b0ef711a5f774\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@82901442916cd9aa0a85cf88d058b89f5506a1fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -257,10 +281,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.516436Z", - "iopub.status.busy": "2024-09-06T19:43:14.515941Z", - "iopub.status.idle": "2024-09-06T19:43:14.519305Z", - "shell.execute_reply": "2024-09-06T19:43:14.518871Z" + "iopub.execute_input": "2024-09-26T14:57:17.190094Z", + "iopub.status.busy": "2024-09-26T14:57:17.189576Z", + "iopub.status.idle": "2024-09-26T14:57:17.193093Z", + "shell.execute_reply": "2024-09-26T14:57:17.192623Z" } }, "outputs": [], @@ -310,10 +334,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.521508Z", - "iopub.status.busy": "2024-09-06T19:43:14.521171Z", - "iopub.status.idle": "2024-09-06T19:43:14.524052Z", - "shell.execute_reply": "2024-09-06T19:43:14.523615Z" + "iopub.execute_input": "2024-09-26T14:57:17.194944Z", + "iopub.status.busy": "2024-09-26T14:57:17.194599Z", + "iopub.status.idle": "2024-09-26T14:57:17.197554Z", + "shell.execute_reply": "2024-09-26T14:57:17.197086Z" }, "nbsphinx": "hidden" }, @@ -331,10 +355,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:14.526149Z", - "iopub.status.busy": "2024-09-06T19:43:14.525818Z", - "iopub.status.idle": "2024-09-06T19:43:23.627822Z", - "shell.execute_reply": "2024-09-06T19:43:23.627249Z" + "iopub.execute_input": "2024-09-26T14:57:17.199051Z", + "iopub.status.busy": "2024-09-26T14:57:17.198872Z", + "iopub.status.idle": "2024-09-26T14:57:26.446906Z", + "shell.execute_reply": "2024-09-26T14:57:26.446343Z" } }, "outputs": [], @@ -408,10 +432,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.630427Z", - "iopub.status.busy": "2024-09-06T19:43:23.630129Z", - "iopub.status.idle": "2024-09-06T19:43:23.635623Z", - "shell.execute_reply": "2024-09-06T19:43:23.635160Z" + "iopub.execute_input": "2024-09-26T14:57:26.449170Z", + "iopub.status.busy": "2024-09-26T14:57:26.448693Z", + "iopub.status.idle": "2024-09-26T14:57:26.454297Z", + "shell.execute_reply": "2024-09-26T14:57:26.453763Z" }, "nbsphinx": "hidden" }, @@ -451,10 +475,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.637682Z", - "iopub.status.busy": "2024-09-06T19:43:23.637404Z", - "iopub.status.idle": "2024-09-06T19:43:23.985761Z", - "shell.execute_reply": "2024-09-06T19:43:23.985192Z" + "iopub.execute_input": "2024-09-26T14:57:26.456078Z", + "iopub.status.busy": "2024-09-26T14:57:26.455769Z", + "iopub.status.idle": "2024-09-26T14:57:26.817319Z", + "shell.execute_reply": "2024-09-26T14:57:26.816634Z" } }, "outputs": [], @@ -491,10 +515,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.988095Z", - "iopub.status.busy": "2024-09-06T19:43:23.987906Z", - "iopub.status.idle": "2024-09-06T19:43:23.992118Z", - "shell.execute_reply": "2024-09-06T19:43:23.991556Z" + "iopub.execute_input": "2024-09-26T14:57:26.819374Z", + "iopub.status.busy": "2024-09-26T14:57:26.819176Z", + "iopub.status.idle": "2024-09-26T14:57:26.823791Z", + "shell.execute_reply": "2024-09-26T14:57:26.823316Z" } }, "outputs": [ @@ -566,10 +590,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:23.994018Z", - "iopub.status.busy": "2024-09-06T19:43:23.993843Z", - "iopub.status.idle": "2024-09-06T19:43:26.637725Z", - "shell.execute_reply": "2024-09-06T19:43:26.636888Z" + "iopub.execute_input": "2024-09-26T14:57:26.825588Z", + "iopub.status.busy": "2024-09-26T14:57:26.825150Z", + "iopub.status.idle": "2024-09-26T14:57:29.558927Z", + "shell.execute_reply": "2024-09-26T14:57:29.558069Z" } }, "outputs": [], @@ -591,10 +615,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.641128Z", - "iopub.status.busy": "2024-09-06T19:43:26.640324Z", - "iopub.status.idle": "2024-09-06T19:43:26.644620Z", - "shell.execute_reply": "2024-09-06T19:43:26.644038Z" + "iopub.execute_input": "2024-09-26T14:57:29.561613Z", + "iopub.status.busy": "2024-09-26T14:57:29.560961Z", + "iopub.status.idle": "2024-09-26T14:57:29.565280Z", + "shell.execute_reply": "2024-09-26T14:57:29.564687Z" } }, "outputs": [ @@ -630,10 +654,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.646963Z", - "iopub.status.busy": "2024-09-06T19:43:26.646497Z", - "iopub.status.idle": "2024-09-06T19:43:26.651999Z", - "shell.execute_reply": "2024-09-06T19:43:26.651546Z" + "iopub.execute_input": "2024-09-26T14:57:29.567105Z", + "iopub.status.busy": "2024-09-26T14:57:29.566772Z", + "iopub.status.idle": "2024-09-26T14:57:29.572163Z", + "shell.execute_reply": "2024-09-26T14:57:29.571688Z" } }, "outputs": [ @@ -811,10 +835,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.654071Z", - "iopub.status.busy": "2024-09-06T19:43:26.653731Z", - "iopub.status.idle": "2024-09-06T19:43:26.680854Z", - "shell.execute_reply": "2024-09-06T19:43:26.680272Z" + "iopub.execute_input": "2024-09-26T14:57:29.573957Z", + "iopub.status.busy": "2024-09-26T14:57:29.573552Z", + "iopub.status.idle": "2024-09-26T14:57:29.601023Z", + "shell.execute_reply": "2024-09-26T14:57:29.600416Z" } }, "outputs": [ @@ -916,10 +940,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.683063Z", - "iopub.status.busy": "2024-09-06T19:43:26.682748Z", - "iopub.status.idle": "2024-09-06T19:43:26.687165Z", - "shell.execute_reply": "2024-09-06T19:43:26.686677Z" + "iopub.execute_input": "2024-09-26T14:57:29.602952Z", + "iopub.status.busy": "2024-09-26T14:57:29.602606Z", + "iopub.status.idle": "2024-09-26T14:57:29.607644Z", + "shell.execute_reply": "2024-09-26T14:57:29.607163Z" } }, "outputs": [ @@ -993,10 +1017,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:26.689077Z", - "iopub.status.busy": "2024-09-06T19:43:26.688908Z", - "iopub.status.idle": "2024-09-06T19:43:28.095086Z", - "shell.execute_reply": "2024-09-06T19:43:28.094529Z" + "iopub.execute_input": "2024-09-26T14:57:29.609321Z", + "iopub.status.busy": "2024-09-26T14:57:29.608970Z", + "iopub.status.idle": "2024-09-26T14:57:31.052597Z", + "shell.execute_reply": "2024-09-26T14:57:31.052050Z" } }, "outputs": [ @@ -1168,10 +1192,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-09-06T19:43:28.097561Z", - "iopub.status.busy": "2024-09-06T19:43:28.097109Z", - "iopub.status.idle": "2024-09-06T19:43:28.101190Z", - "shell.execute_reply": "2024-09-06T19:43:28.100749Z" + "iopub.execute_input": "2024-09-26T14:57:31.054589Z", + "iopub.status.busy": "2024-09-26T14:57:31.054180Z", + "iopub.status.idle": "2024-09-26T14:57:31.058507Z", + "shell.execute_reply": "2024-09-26T14:57:31.057947Z" }, "nbsphinx": "hidden" }, @@ -1204,7 +1228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/versioning.js b/versioning.js index 0d909dd7e..4f9ffb8d0 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "9c563ed5c55574f3f6fa5ce0532b0ef711a5f774", + commit_hash: "82901442916cd9aa0a85cf88d058b89f5506a1fb", }; \ No newline at end of file