hila-chefer/Transformer-Explainability

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2014 stars · 261 forks observed · 2026-08-28

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

Official PyTorch implementation of the CVPR 2021 paper 'Transformer Interpretability Beyond Attention Visualization', providing relevance-based explanations for classifications made by Transformer models like ViT and BERT. It ships as Jupyter/Colab notebooks demonstrating per-class explanation visualization for vision and NLP tasks.

Use cases

• visualize which image patches a ViT used for its classification
• explain BERT token classifications with relevance heatmaps
• generate per-class explanations for transformer predictions
• understand transformer attention beyond raw attention maps
• reproduce CVPR 2021 transformer interpretability research
• add explainability to DeiT or other vision transformers

When to choose

• you need research-grade relevance/saliency explanations for ViT or BERT models
• you want per-class visual explanations rather than plain attention visualization
• you want runnable Colab notebooks to experiment quickly

When to avoid

• you need a production-ready, maintained explainability library with broad model support
• you need explainability for non-PyTorch or multimodal/encoder-decoder transformers (see the authors' follow-up work)
• you need an actively developed tool - the repo is primarily a paper artifact

Facets

library · maturity maintenance

machine-learning nlp image-processing data-visualization deep-learning computer-vision artificial-intelligence python explainability vision-transformer bert attention-visualization interpretability cvpr-2021 pytorch research-code natural-language-processing

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