---

name: toxigen_classifier_recovery description: Run a bounded ToxiGen-style classifier update and report validator-compatible before-after loss, AUC, and parameter evidence.

ToxiGen Classifier Recovery

Use this skill when a recovery experiment needs to demonstrate the paper's classifier-improvement mechanism without full HateBERT or RoBERTa fine-tuning. It trains a tiny logistic classifier over deterministic text features and emits before/after metrics.

Do not use this skill to claim full paper Table 4 reproduction. A result from this skill is a reduced proxy unless it is replaced by a real model fine-tuning stack.

Inputs

• Generated or curated examples with text and label.
• Optional group and generation method metadata.
• Learning rate and update-step count.

Outputs

• loss_before, loss_after, auc_before, auc_after.
• params_before, params_after, and optimizer_state_changed.
• Per-example probabilities before and after the update.

Workflow

1. Extract deterministic features from each text: bias/toxicity cue count, benign cue count, identity cue count, and normalized length.
2. Initialize logistic weights and bias to zero unless supplied.
3. Compute binary cross-entropy and pairwise AUC.
4. Run bounded gradient-descent updates.
5. Recompute metrics and emit a trace with parameter-change evidence.

Validation

Run:

python scripts/classifier_recovery.py --self-test
python tests/test_classifier_recovery.py

The tests verify that parameters change and that loss does not increase on a simple fixture.

Limitations

This skill is intentionally small and deterministic. It is useful for mechanism-faithful soft recovery, edge-case tests, and harness validation, not for reporting deployed toxicity classifier performance.