[docs]def clip_values(x, low=0.0, high=1.0, new_sum: Optional[float] = None) -> np.ndarray:
"""Clip all values in p to range [low,high].
Preserves sum of x.
@@ -729,17 +729,14 @@
Source code for cleanlab.internal.util
x : np.ndarray
A list of clipped values, summing to the same sum as x."""
- def clip_range(a, low=low, high=high):
- """Clip a into range [low,high]"""
- return min(max(a, low), high)
-
- vectorized_clip = np.vectorize(
- clip_range
- ) # Vectorize clip_range for efficiency with np.ndarrays
- prev_sum = sum(x) if new_sum is None else new_sum # Store previous sum
- x = vectorized_clip(x) # Clip all values (efficiently)
+ if len(x.shape) > 1:
+ raise TypeError(
+ f"only size-1 arrays can be converted to Python scalars but 'x' had shape {x.shape}"
+ )
+ prev_sum = np.sum(x) if new_sum is None else new_sum # Store previous sum
+ x = np.clip(x, low, high) # Clip all values (efficiently)
x = (
- x * prev_sum / np.clip(float(sum(x)), a_min=TINY_VALUE, a_max=None)
+ x * prev_sum / np.clip(np.sum(x), a_min=TINY_VALUE, a_max=None)
) # Re-normalized values to sum to previous sum
return x
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb
index 46b37d704..828649547 100644
--- a/master/_sources/tutorials/clean_learning/tabular.ipynb
+++ b/master/_sources/tutorials/clean_learning/tabular.ipynb
@@ -120,7 +120,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb
index ede05464f..f1f31cf72 100644
--- a/master/_sources/tutorials/clean_learning/text.ipynb
+++ b/master/_sources/tutorials/clean_learning/text.ipynb
@@ -129,7 +129,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb
index 8111dc9de..e823f64a1 100644
--- a/master/_sources/tutorials/datalab/audio.ipynb
+++ b/master/_sources/tutorials/datalab/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index 59d67f1f0..4d748d34d 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index b77f310a5..a73e18182 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index 9b208f9fe..42b41bfef 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -80,7 +80,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index 469ab488a..ee1f8403b 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index c59730731..d032d6e58 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -79,7 +79,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index 6ca13ceb0..3be3a80a0 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index 8f5165de5..4c772eda2 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 5d2685b6c..40a6de3be 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 1f90e9ca9..4724bd561 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 97e4513a6..3e0188c7e 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index bb22c545c..0b98b27bd 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -110,7 +110,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index d2b0bbb42..3f71203b8 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index d8a611d23..b7d1965d7 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index ed2af2ad8..c313406d3 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", 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"cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", 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Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. 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Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. 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Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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"color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], 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[[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"], [96, "id8"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Identify Spurious Correlations in Image Datasets": [[96, "Identify-Spurious-Correlations-in-Image-Datasets"]], "2. Creating Dataset object to be passed to the Datalab object to find image-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-image-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Image-specific property scores in the original dataset": [[96, "Image-specific-property-scores-in-the-original-dataset"]], "Image-specific property scores in the transformed dataset": [[96, "Image-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": 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cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb
index aa00ed6e9..3c2c0a9bd 100644
--- a/master/tutorials/clean_learning/tabular.ipynb
+++ b/master/tutorials/clean_learning/tabular.ipynb
@@ -113,10 +113,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T15:09:49.406100Z",
- "iopub.status.busy": "2024-07-02T15:09:49.405638Z",
- "iopub.status.idle": "2024-07-02T15:09:50.626225Z",
- "shell.execute_reply": "2024-07-02T15:09:50.625679Z"
+ "iopub.execute_input": "2024-07-02T15:24:50.264127Z",
+ "iopub.status.busy": "2024-07-02T15:24:50.263741Z",
+ "iopub.status.idle": "2024-07-02T15:24:51.435602Z",
+ "shell.execute_reply": "2024-07-02T15:24:51.435065Z"
},
"nbsphinx": "hidden"
},
@@ -126,7 +126,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@e67c4aeedd6310b5ad112e4c90674400bc877e0e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@c915f776420f13284807e915043326eda337d0c4\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -151,10 +151,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-07-02T15:09:50.628776Z",
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@@ -195,10 +195,10 @@
"execution_count": 3,
"metadata": {
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@@ -305,10 +305,10 @@
"execution_count": 4,
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@@ -329,10 +329,10 @@
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@@ -384,10 +384,10 @@
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@@ -409,10 +409,10 @@
"execution_count": 7,
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@@ -446,10 +446,10 @@
"execution_count": 8,
"metadata": {
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"outputs": [
@@ -481,10 +481,10 @@
"execution_count": 9,
"metadata": {
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"outputs": [
@@ -605,10 +605,10 @@
"execution_count": 10,
"metadata": {
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@@ -633,10 +633,10 @@
"execution_count": 11,
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@@ -658,10 +658,10 @@
"execution_count": 12,
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"outputs": [
@@ -691,10 +691,10 @@
"execution_count": 13,
"metadata": {
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"outputs": [],
@@ -715,10 +715,10 @@
"execution_count": 14,
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"outputs": [],
@@ -738,10 +738,10 @@
"execution_count": 15,
"metadata": {
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"outputs": [
@@ -771,10 +771,10 @@
"execution_count": 16,
"metadata": {
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diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html
index 9c18d0c40..2bf9854d9 100644
--- a/master/tutorials/clean_learning/text.html
+++ b/master/tutorials/clean_learning/text.html
@@ -817,7 +817,7 @@
2. Load and format the text dataset