README.md

metadata

title: Bertographer
emoji: 🔭
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.50.0
app_file: app.py
pinned: false
license: mit
short_description: Mechanistic-interpretability cockpit for encoder models

Bertographer

A mechanistic-interpretability cockpit for encoder classifiers (BERT / RoBERTa / DeBERTa / DistilBERT / ELECTRA), framed in production-observability language rather than lab notation.

Where a decoder probe reads a model's next-token futures, Bertographer reads an encoder's decision: for every layer and attention head it projects the pooling token's register into two spaces —

• vocab (logit-lens via the tied word embeddings) — what token this register "says"
• class (the model's own classification head) — which label this head votes for

…and lets an operator intervene (mute / scale a head) and watch the prediction move. The framing is the engine room, not the notebook:

• input = a trace · the layer stack = the waterfall · heads = spans / voters
• mute / scale = the counterfactual ("what if the model hadn't seen this evidence?")
• compare = a trace‑vs‑trace diff ("what differed between this request and the last?")

Default model: cardiffnlp/twitter-roberta-base-sentiment-latest (3‑class sentiment). Architecture is auto‑discovered; per‑head views degrade gracefully when a model's attention output isn't a clean per‑head concatenation.

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Built by James J. Davison, with Claude (Anthropic) as coding collaborator. Responsibility for the code and its design choices rests with the human author. MIT licensed. © 2026 James J. Davison.