cdpierse/transformers-interpret

Health v2 · maintenance only

Adoption not part of the score

1416 stars · 99 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library providing model explainability for Hugging Face Transformers models, built on Captum. It explains text classification, question answering, and computer vision models in two lines of code, with visualizations in notebooks or as savable PNG/HTML files.

Use cases

• explain transformer model predictions with token attributions
• visualize which words drive a sentiment classifier's output
• interpret question answering model answers
• explain image classification models built on transformers
• generate attribution visualizations for NLP models in notebooks
• debug why a fine-tuned model makes certain predictions

When to choose

• you use Hugging Face transformers and want quick, minimal-code model explanations
• you need token-level or pixel-level attribution visualizations
• you want Captum-based interpretability without writing Captum boilerplate

When to avoid

• your models are not from the transformers library
• you need explainability for non-neural or non-PyTorch models
• you need actively maintained support for the latest transformers versions

Facets

library · maturity maintenance

machine-learning nlp deep-learning developer-tools machine-learning computer-vision deep-learning python explainable-ai interpretability captum huggingface-transformers model-explainability attribution natural-language-processing

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