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Cleanlab’s open-source library helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models.
 


  


    Examples of various issues in Cat/Dog dataset automatically detected by cleanlab via this code:    


python
        lab = cleanlab.Datalab(data=dataset, label="column_name_for_labels")
         Fit any ML model, get its feature_embeddings & pred_probs for your data
        lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
        lab.report()


- Use cleanlab to automatically check every: text, audio, image, or tabular dataset.
- Use cleanlab to automatically: detect data issues (outliers, duplicates, label errors, etc), train robust models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning).


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 Run cleanlab open-source

This cleanlab package runs on Python 3.10+ and supports Linux, macOS, as well as Windows.

- Get started here! Install via uv, pip, or conda.
- Developers who install the bleeding-edge from source should refer to this master branch documentation.

Practicing data-centric AI can look like this:
1. Train initial ML model on original dataset.
2. Utilize this model to diagnose data issues (via cleanlab methods) and improve the dataset.
3. Train the same model on the improved dataset. 
4. Try various modeling techniques to further improve performance.

Most folks jump from Step 1 → 4, but you may achieve big gains without any change to your modeling code by using cleanlab!
Continuously boost performance by iterating Steps 2 → 4 (and try to evaluate with cleaned data).

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 Use cleanlab with any model and in most ML tasks

All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc.

cleanlab is useful across a wide variety of Machine Learning tasks. Specific tasks this data-centric AI package offers dedicated functionality for include:
1. Binary and multi-class classification
2. Multi-label classification (e.g. image/document tagging)
3. Token classification (e.g. entity recognition in text)
4. Regression (predicting numerical column in a dataset)
5. Image segmentation (images with per-pixel annotations)
6. Object detection (images with bounding box annotations)
7. Classification with data labeled by multiple annotators
8. Active learning with multiple annotators (suggest which data to label or re-label to improve model most)
9. Outlier detection (identify atypical data that appears out of distribution)

For other ML tasks, cleanlab can still help you improve your dataset if appropriately applied.
See our Example Notebooks and Blog.


 So fresh, so cleanlab

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