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.

翻译：本报告介绍了一种注意力可视化工具包，该工具包旨在直观展示仅编码器Transformer模型中各单词的重要性。与其他侧重于词元及自注意力分数的方法不同，我们的方法将考察单词及其对最终嵌入表示的影响。此类库在增强神经网络可解释性与可说明性方面发挥着关键作用。它们为揭示网络内部机制提供了契机，有助于更深入地理解其运作方式及改进途径。您可通过以下GitHub仓库访问代码并查看示例：https://github.com/AlaFalaki/AttentionVisualizer。