cleanlab

We have hosted the application cleanlab in order to run this application in our online workstations with Wine or directly.

Quick description about cleanlab:

cleanlab 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. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box.

Features:

• Binary and multi-class classification
• Multi-label classification (e.g. image/document tagging)
• Token classification (e.g. entity recognition in text)
• Classification with data labeled by multiple annotators
• Active learning with multiple annotators (suggest which data to label or re-label to improve model most)
• Outlier and out of distribution detection

Programming Language: Python.
Categories: