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arXiv · 2308.14850

Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models

Ala Alam Falaki·Robin Gras
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Abstract

This report introduces the Attention Visualizer package, which is crafted to visually illustrate the significance of individual words in encoder-only transformer-based models. In contrast to other methods that center on tokens and self-attention scores, our approach will examine the words and their impact on the final embedding representation. Libraries like this play a crucial role in enhancing the interpretability and explainability of neural networks. They offer the opportunity to illuminate their internal mechanisms, providing a better understanding of how they operate and can be enhanced. You can access the code and review examples on the following GitHub repository: https://github.com/AlaFalaki/AttentionVisualizer.

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Ala Alam Falaki, Robin Gras. 2023-08-28. Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models. https://arxiv.org/abs/2308.14850

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