hila-chefer / Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Date Created 2020-11-23 (3 years ago)

Commits 86 (last one about a year ago)

Stargazers 1,768 (5 this week)

Watchers 21 (0 this week)

Forks 239

License mit

Ranking

RepositoryStats indexes 565,600 repositories, of these hila-chefer/Transformer-Explainability is ranked #28,587 (95th percentile) for total stargazers, and #104,531 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #616/16,305.

hila-chefer/Transformer-Explainability is also tagged with popular topics, for these it's ranked: deep-learning (#815/8173), bert (#55/549)

Other Information

hila-chefer/Transformer-Explainability has 4 open pull requests on Github, 3 pull requests have been merged over the lifetime of the repository.

Github issues are enabled, there are 9 open issues and 53 closed issues.

All Topics

vit bert cvpr2021 bert-model perturbation deep-learning explainability attention-matrix vision-transformer attention-visualization visualize-classifications transformer-interpretability

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13 commits on the default branch (main) since jan '22

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updated: 2024-09-29 @ 02:35pm, id: 315439501 / R_kgDOEs05jQ