| Name | explainable-attention JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | Implementation of various tools for multi-head attention explainability from transformers. |
| upload_time | 2025-08-10 18:11:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# explainable-attention
Implementation of various tools for multi-head attention explainability from transformers.
## Self-Attention Attribution
[Hao, Yaru, et al. "Self-attention attribution: Interpreting information interactions inside transformer." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 14. 2021.](https://arxiv.org/abs/2004.11207)
```py
from explainable_attention.self_attention_attribution import compute
...
def objective(batch):
x, y = batch
y = model(x)
loss = loss_fn(x, y)
return loss
attribution = saa.compute(
model.transformer_encoder.layers,
objective,
batch,
integration_steps=20)
```
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