Attention Heatmap
Chart overview
Attention heatmaps render the weight matrix that a transformer's attention head assigns between every input token pair, revealing which parts of a sequence the model focuses on.
Key points
- Researchers use them to interpret language model behavior, debug unexpected predictions, and communicate model reasoning in scientific NLP papers.
- They are equally applicable to biological sequence models and vision transformers.
Example Visualization
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Generate publication-ready attention heatmaps with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create an attention heatmap from my attention weight matrix. Label both axes with token strings, use a sequential colormap (viridis or YlOrRd), annotate cells with weight values rounded to 2 decimal places, and add a colorbar. Show one attention head per subplot if multiple heads are provided."
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Python Code Example
Console Output
Figure saved: plotivy-attention-heatmap.png
Common Use Cases
- 1Inspecting which input tokens a BERT model attends to when predicting a masked word
- 2Debugging cross-attention alignment in a neural machine translation model
- 3Visualizing structural attention patterns in protein language models
- 4Comparing attention distributions across multiple heads in a vision transformer
Pro Tips
Normalize each row to sum to 1 so each token attention distribution is comparable
Use a log scale for attention weights when values are heavily skewed toward a few tokens
Display multiple heads in a grid subplot to identify head specialization patterns
Mask the upper or lower triangle for causal models to reflect true information flow
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.