jessevig/bertviz
BertViz: Visualize Attention in Transformer Models
Provides three complementary visualization modes—head view (individual attention heads), model view (all layers/heads overview), and neuron view (query/key vector decomposition)—enabling multi-level analysis of transformer attention mechanisms. Integrates directly with Huggingface transformers via a simple Python API that works in Jupyter and Colab notebooks by extracting attention tensors from model outputs. Supports both encoder-only models (BERT, GPT-2) and encoder-decoder architectures (BART, T5) with interactive HTML visualizations.
7,945 stars. Available on PyPI.
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7,945
Forks
871
Language
Python
License
Apache-2.0
Last pushed
Jan 08, 2026
Commits (30d)
0
Dependencies
8
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