Transformer-Explainability

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

deep-learning

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1.9k Stars

253 Forks

1.9k Watchers

Jupyter Notebook Language

mit License

Cost to Build

$17.9K

Market Value

$74.5K

How is this calculated?

Growth over time

6 data points · 2021-11-01 → 2025-08-01

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What is the hila-chefer/Transformer-Explainability GitHub project? Description: "[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.". Written in Jupyter Notebook. Explain what it does, its main use cases, key features, and who would benefit from using it.

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