wehub-resource-sync
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""BBQ (Bias Benchmark for QA) benchmark.
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Tests social bias across 11 categories including age, gender,
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race, religion, disability, nationality, and more.
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3-choice multiple choice format.
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Dataset bundled from lighteval/bbq_helm on HuggingFace.
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"""
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import logging
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from pathlib import Path
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from typing import Optional
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from .base import BaseBenchmark
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from .datasets import deterministic_sample, load_jsonl
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logger = logging.getLogger(__name__)
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DATA_DIR = Path(__file__).parent / "data"
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class BBQBenchmark(BaseBenchmark):
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"""BBQ: 0-shot bias detection with 3 choices."""
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name = "bbq"
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quick_size = 300
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async def load_dataset(self, sample_size: int = 0) -> list[dict]:
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"""Load BBQ from bundled data."""
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items = load_jsonl(DATA_DIR / "bbq_test.jsonl")
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normalized = []
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for item in items:
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choices = item.get("choices", [])
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labels = item.get("labels", [])
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if not choices or not labels:
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continue
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normalized.append({
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"id": item.get("id", ""),
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"context": item["context"],
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"question": item["question"],
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"choices": choices,
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"labels": labels,
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"answer": item["answer"],
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"category": item.get("category", "unknown"),
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})
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logger.info(f"BBQ: loaded {len(normalized)} questions")
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if sample_size == 0:
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return normalized
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return deterministic_sample(normalized, sample_size)
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def format_prompt(self, item: dict) -> list[dict[str, str]]:
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"""Format as context + question with 3 choices."""
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context = item["context"]
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question = item["question"]
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choices = item["choices"]
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labels = item["labels"]
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parts = [
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"Read the context and answer the question. "
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"Answer with just the letter.\n",
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f"Context: {context}\n",
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f"Question: {question}\n",
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]
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for label, choice in zip(labels, choices):
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parts.append(f"{label}. {choice}")
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parts.append("\nAnswer:")
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return [{"role": "user", "content": "\n".join(parts)}]
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def extract_answer(self, response: str, item: dict) -> str:
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valid = item.get("labels", ["A", "B", "C"])
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return self._extract_mc_answer(response, valid)
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def check_answer(self, predicted: str, item: dict) -> bool:
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return predicted == item["answer"]
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def get_question_text(self, item: dict) -> str:
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return f"{item.get('context', '')} {item.get('question', '')}"
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def get_category(self, item: dict) -> Optional[str]:
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return item.get("category")
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