  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.

Computation and Language;Artificial Intelligence