#2020th of 20 in Data and scraping for AI
cleanlab
Python library that finds label errors and outliers in ML datasets
- Stars
- 11.7k
- License
- Apache-2.0
- Last commit
- Jan 2026
- Last release
- Jan 2026
- Language
- Python
11,340 on 31 Mar 202611,713 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
cleanlab uses predictions and embeddings from your existing model to detect label errors, outliers, duplicates and other issues in text, image, audio and tabular datasets. It is based on confident learning and also offers CleanLearning, a wrapper that trains a classifier on the cleaned data, plus tools for multi-annotator consensus and active learning.
Who it is for: ML engineers cleaning noisy labels in training datasets
Strengths
- Model-agnostic: works with outputs from PyTorch, TensorFlow, JAX, HuggingFace, XGBoost, scikit-learn
- Dedicated support for multi-label, token classification, regression, segmentation and object detection
- Datalab API reports issues in a few lines: find_issues() then report()
- Algorithms are backed by published peer-reviewed papers
Weaknesses
- Needs pred_probs or embeddings from a trained model; it does not train a base model for you
- Aimed at supervised ML datasets; not a general document or web data cleaner
- Other ML tasks beyond the listed ones require adapting the methods
- Library only; no hosted UI or web app in the open-source package
What it needs
- no GPU
- Needs Python 3.10+
- Models: PyTorch, TensorFlow, Keras, JAX, HuggingFace
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