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bertviz is a Python library for interactive visualization of attention in Transformer-based NLP models. It provides three complementary views: Head View (per-head attention patterns), Model View (attention flow between tokens across layers), and Neuron View (query/key interactions within individual attention heads). The library is designed for Jupyter notebooks, rendering D3.js-based interactive HTML directly in cell output. It accepts attention tensors from most HuggingFace models via output_attentions=True, and includes a vendored, modified copy of older HuggingFace transformers code to expose query/key vectors for the Neuron View on BERT, GPT-2, RoBERTa, XLNet, and similar architectures.
RATING:
MAINT: maintained
DRIFT: high
▸ 3 reusable components found
── end of results ──