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A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard. Interpret-Text builds on Interpret, an open source python package for training interpretable models and helping to explain blackbox machine learning systems. We have added extensions to support text models. Interpret-Text incorporates community-developed interpretability techniques for NLP models and a visualization dashboard to view the results. Users can run their experiments across multiple state-of-the-art explainers and easily perform comparative analysis on them. Using these tools, users will be able to explain their machine-learning models globally on each label or locally for each document.
Features
- Actively incorporates innovative text interpretability techniques, and allows the community to further expand its offerings
- Creates a common API across the integrated libraries
- Provides an interactive visualization dashboard to empower its users to gain insights into their data
- Currently this repository only provides support for the text classification scenario
- Linear models with support for a 'coefs_' call under sklearn's linear_model module
- Tree based models with a 'feature_importances' call under sklearn's ensemble module
Project Samples
