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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>
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
import json
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import os
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from os import environ
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from datasets import Dataset, DatasetDict
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from opencompass.registry import LOAD_DATASET
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from opencompass.utils import get_data_path
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from .base import BaseDataset
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@LOAD_DATASET.register_module()
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class commonsenseqaDataset(BaseDataset):
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@staticmethod
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def load(path):
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path = get_data_path(path)
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if environ.get('DATASET_SOURCE') == 'ModelScope':
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from modelscope import MsDataset
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dataset = {}
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for split in ['train', 'validation']:
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ms_dataset = MsDataset.load(path, split=split)
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dataset_list = []
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for line in ms_dataset:
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choices = line['choices']
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dataset_list.append({
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'question': line['question'],
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'A': choices['text'][0],
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'B': choices['text'][1],
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'C': choices['text'][2],
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'D': choices['text'][3],
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'E': choices['text'][4],
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'answerKey': line['answerKey'],
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})
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dataset[split] = Dataset.from_list(dataset_list)
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else:
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dataset = {}
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for split, stub in [
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['train', 'train_rand_split.jsonl'],
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['validation', 'dev_rand_split.jsonl'],
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]:
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data_path = os.path.join(path, stub)
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dataset_list = []
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with open(data_path, 'r', encoding='utf-8') as f:
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for line in f:
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line = json.loads(line)
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dataset_list.append({
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'question':
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line['question']['stem'],
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'A':
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line['question']['choices'][0]['text'],
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'B':
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line['question']['choices'][1]['text'],
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'C':
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line['question']['choices'][2]['text'],
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'D':
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line['question']['choices'][3]['text'],
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'E':
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line['question']['choices'][4]['text'],
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'answerKey':
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line['answerKey'],
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})
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dataset[split] = Dataset.from_list(dataset_list)
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return DatasetDict(dataset)
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