Project Title

cleanlab — Data-centric AI for Cleaning and Labeling Real-World Data

Overview

Cleanlab is an open-source Python library designed to address data quality issues in machine learning datasets. It leverages existing models to detect and correct problems in datasets, enabling the training of more reliable and accurate models. This data-centric AI package is particularly useful for handling messy, real-world data and labels, improving the reliability of supervised learning, LLM, and RAG applications.

Key Features

Automatic detection of data issues such as outliers, duplicates, and label errors.
Utilization of existing ML models to estimate dataset problems.
Improvement of dataset quality to train better models.
Support for text, audio, image, and tabular datasets.
Capabilities for detecting data issues, training robust models, inferring consensus and annotator quality, and suggesting data for (re)labeling.

Use Cases

Data scientists and machine learning engineers using real-world datasets to improve model reliability and accuracy.
Researchers in fields requiring high data quality, such as healthcare and finance, to ensure their models are trained on clean and accurate data.
Companies dealing with multi-annotator data to infer consensus and annotator quality, enhancing the trustworthiness of their data.

Advantages

Enhances model performance by improving dataset quality without changing the modeling code.
Saves time and resources by automatically detecting and suggesting fixes for data issues.
Broad applicability across various data types, including text, audio, image, and tabular data.

Limitations / Considerations

The library's effectiveness depends on the quality of the initial ML model used for dataset diagnosis.
May require significant computational resources for large datasets.
The accuracy of data issue detection can be influenced by the complexity and noise in the data.

Snorkel: A system for rapidly creating, testing, and maintaining information extraction applications. Unlike cleanlab, Snorkel focuses more on labeling functions and less on data quality improvement.
Dedupe: A library for de-duplicating data. While Dedupe is useful for identifying duplicates, cleanlab offers a broader range of data quality improvement features.

Basic Information

GitHub: https://github.com/cleanlab/cleanlab
Stars: 10,696
License: Unknown
Last Commit: 2025-07-16

📊 Project Information

Project Name: cleanlab
GitHub URL: https://github.com/cleanlab/cleanlab
Programming Language: Python
⭐ Stars: 10,696
🍴 Forks: 835
📅 Created: 2018-05-11
🔄 Last Updated: 2025-07-16

🏷️ Project Topics

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📚 Documentation

suggest data to (re)label next (active learning)

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cleanlab

Project Description