Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models
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.
Code (1)
Similar Papers 제목 키워드 기반
MidiTok Visualizer: a tool for visualization and analysis of tokenized MIDI symbolic music
Symbolic music research plays a crucial role in music-related machine learning, but MIDI data can be complex for those without musical expertise. To address this issue, we present MidiTok Visualizer, a web application de…
IndoWordnet Visualizer: A Graphical User Interface for Browsing and Exploring Wordnets of Indian Languages
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-fr…
Decision MakingExplainable artificial intelligenceNeuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method
In recent years, there has been a growing interest in visualizing the loss landscape of neural networks. Linear landscape visualization methods, such as principal component analysis, have become widely used as they intui…