Cleanlab is a Python library for automatically detecting and fixing data quality issues in machine learning datasets.
The tool addresses the problem that real-world datasets contain mislabeled examples, outliers, duplicates, and other issues that degrade model performance. Rather than requiring manual inspection, cleanlab uses predictions from existing models to estimate which data points are problematic. It works across multiple data modalities including text, audio, images, and tabular data, identifying label errors, out-of-distribution examples, duplicates, and other anomalies. The library also supports multi-annotator scenarios by inferring consensus labels and annotator quality, and can suggest which examples to label next through active learning.
Teams should adopt cleanlab when working with messy, real-world datasets where label quality is uncertain and manual review is impractical. It fits projects where you can train an initial model quickly and want to iteratively improve data quality rather than endlessly tuning model architecture. The approach works best when you have some labeled data to begin with, as the library leverages model predictions to identify issues. The workflow involves training a model, using cleanlab to diagnose dataset problems, cleaning the data, and retraining—a cycle that can repeat until diminishing returns appear.
The project maintains active development with regular updates to its core functionality. The codebase includes comprehensive test coverage and documentation spanning tutorials for different data types and use cases. The team publishes research backing the methods and maintains an examples repository demonstrating practical applications. Development activity shows consistent attention to both the library's core algorithms and its usability across different data modalities.
