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

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

URL: http://arxiv.org/abs/2308.14850v1
Date: Mon, 28 Aug 2023 19:11:52 GMT
Title: Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models
Authors: Ala Alam Falaki, and Robin Gras
Abstract summary: This report introduces the Attention Visualizer package. It is crafted to visually illustrate the significance of individual words in encoder-only transformer-based models.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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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