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name: deberta_race_multiple_choice_protocol description: Build and evaluate RACE style multiple choice candidate records for DeBERTa recovery experiments.

DeBERTa RACE Multiple Choice Protocol

Use this skill when a DeBERTa recovery needs a RACE-style article, question, four-option candidate layout, and accuracy calculation. The skill owns label handling and metric computation; attention and decoder helpers should return scores, not final benchmark semantics.

Inputs

• Article text.
• Question text.
• Four answer option strings.
• Gold label A, B, C, or D.
• Optional token budget.

Outputs

• Four candidate records containing article, question, option, label, candidate text, token positions, and absolute option position.
• Predicted label from candidate logits.
• Accuracy as a numeric metric.

Workflow

1. Normalize whitespace.
2. Tokenize deterministically for reduced recovery, or preserve supplied tokenizer output in full recovery.
3. Build one candidate per option using the conceptual layout [CLS] article [SEP] question option [SEP].
4. Score candidates externally.
5. Use scripts/race_protocol.py to choose the maximum-logit label and compute accuracy.

Validation

The tests confirm four-option validation, label preservation, packed candidate records, and exact accuracy behavior.

Limitations

This skill does not download or parse the full RACE benchmark. A reduced recovery item must clearly mark whether it is benchmark-style or resource-derived.

The standard-library tokenizer is only a recovery proxy. When truncation is used, record the requested max_seq_len, the emitted candidate token lengths, and whether absolute positions refer to the original untruncated item or the truncated packed sequence.