interpretml/interpret-text
An open-source library for explaining and visualizing blackbox text classification models using state-of-the-art interpretability techniques.
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Interpret-Text builds on the Interpret ML framework to extend interpretability capabilities to NLP and text-based machine learning models. It provides a unified SDK and Jupyter notebooks that let users run experiments across multiple explainers (glass-box, grey-box, and black-box approaches) and compare results visually. The library supports both global explanations across labels and local explanations for individual documents.
Frequently asked
- What is interpretml/interpret-text?
- An open-source library for explaining and visualizing blackbox text classification models using state-of-the-art interpretability techniques.
- Is interpret-text open source?
- Yes — interpretml/interpret-text is open source, released under the MIT license.
- What language is interpret-text written in?
- interpretml/interpret-text is primarily written in Python.
- How popular is interpret-text?
- interpretml/interpret-text has 432 stars on GitHub.
- Where can I find interpret-text?
- interpretml/interpret-text is on GitHub at https://github.com/interpretml/interpret-text.