Vision Transformer Attention Visualization
Analyze text using tuned lens and visualize predictions
Explore and filter language model benchmark results
Generate insights and visuals from text
Playground for NuExtract-v1.5
Extract relationships and entities from text
Compare LLMs by role stability
Identify named entities in text
Generate relation triplets from text
Electrical Device Feedback Sentiment Classifier
Compare different tokenizers in char-level and byte-level.
Provide feedback on text content
"One-minute creation by AI Coding Autonomous Agent MOUSE"
Attention Visualization is a tool designed to provide insights into how Vision Transformers process text by visualizing the attention mechanisms. It allows users to see which parts of the input text are most relevant for generating responses or making predictions. This tool is particularly useful for understanding the decision-making process of large language models.
What is the purpose of attention visualization?
Attention visualization helps users understand how Vision Transformers focus on different parts of the input text, providing transparency into the model's decision-making process.
Can I use my own model with Attention Visualization?
Yes, Attention Visualization is designed to be model-agnostic, allowing you to use it with various Vision Transformer architectures.
How do I interpret the heatmaps?
Heatmaps display token importance, with darker colors indicating higher attention. This helps identify which parts of the text are more influential in the model's outputs.