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from typing import List |
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from datasets import Dataset |
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from opencompass.openicl import BaseEvaluator |
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from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET |
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from ..base import BaseDataset |
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from .utils import iter_jsonl |
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@LOAD_DATASET.register_module() |
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class InfiniteBenchendiaDataset(BaseDataset): |
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@staticmethod |
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def load(path: str): |
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dataset = list(iter_jsonl(path)) |
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raw_data = [] |
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for item in dataset: |
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context = item['context'] |
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question = item['input'] |
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answer = item['answer'] |
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raw_data.append({ |
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'context': context, |
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'question': question, |
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'answer': answer |
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}) |
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dataset = Dataset.from_list(raw_data) |
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return dataset |
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@ICL_EVALUATORS.register_module() |
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class InfiniteBenchendiaEvaluator(BaseEvaluator): |
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def score(self, predictions: List, references: List) -> dict: |
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score = 0. |
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for i in range(len(predictions)): |
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prediction = predictions[i] |
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reference = references[i][0] |
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for c in ['\n', ':', '"', "'", '.', ',', '?', '!', '{', '}']: |
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prediction = prediction.replace(c, ' ') |
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words = prediction.split() |
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words = [x.upper() for x in words] |
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if reference in words: |
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score += 1 |
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score = score / len(predictions) * 100 |
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return {'score': score} |
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