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edab1c07ba
* add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * udpate dataset for modelscope support * update readme * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * update readme * remove tydiqa japanese subset * add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * update readme * udpate dataset for modelscope support * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * remove tydiqa japanese subset * update util * remove .DS_Store * fix md format * move util into package * update docs/get_started.md * restore eval_api_zhipu_v2.py, add environment setting * Update dataset * Update * Update * Update * Update --------- Co-authored-by: Yun lin <yunlin@U-Q9X2K4QV-1904.local> Co-authored-by: Yunnglin <mao.looper@qq.com> Co-authored-by: Yun lin <yunlin@laptop.local> Co-authored-by: Yunnglin <maoyl@smail.nju.edu.cn> Co-authored-by: zhangsongyang <zhangsongyang@pjlab.org.cn>
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import json
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import os.path as osp
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from datasets import Dataset, DatasetDict
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from opencompass.openicl.icl_evaluator import BaseEvaluator
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from opencompass.utils import get_data_path
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from opencompass.utils.text_postprocessors import general_postprocess
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from .base import BaseDataset
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class NaturalQuestionDatasetCN(BaseDataset):
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@staticmethod
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def load(path: str):
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path = get_data_path(path, local_mode=True)
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dataset = DatasetDict()
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for split in ['dev', 'test']:
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filename = osp.join(path, f'{split}.jsonl')
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all_data = []
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with open(filename, 'r') as f:
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for line in f:
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data = json.loads(line)
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if split == 'dev':
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data['answer'] = data['answer'][0]
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all_data.append(data)
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dataset[split] = Dataset.from_list(all_data)
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return dataset
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class NQEvaluatorCN(BaseEvaluator):
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def score(self, predictions, references):
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if len(predictions) != len(references):
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return {
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'error': 'predictions and references have different '
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'length'
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}
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processed_predictions = []
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for prediction in predictions:
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prediction = prediction.split('\n')[0].lower()
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if '答案是：' in prediction:
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prediction = prediction.split('答案是：')[-1]
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prediction = general_postprocess(prediction)
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processed_predictions.append(prediction)
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processed_answers = [[general_postprocess(j).lower() for j in i]
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for i in references]
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cnt = 0
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for pred, cand_ans in zip(processed_predictions, processed_answers):
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cnt += int(any([cand == pred for cand in cand_ans]))
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score = cnt / len(predictions) * 100
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return {'score': score}
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