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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>
83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
import json
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import re
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import string
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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.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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# Modified from https://github.com/openai/simple-evals/blob/main/drop_eval.py
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ANSWER_PATTERN = r'(?i)Answer\s*:\s*([^\n]+)'
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def normalize(s: str) -> str:
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"""Lower text and remove punctuation, articles and extra whitespace."""
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s = s.lower()
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exclude = set(string.punctuation)
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s = ''.join(char for char in s if char not in exclude)
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s = re.sub(r'\b(a|an|the)\b', ' ', s)
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s = ' '.join(s.split())
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return s
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def fuzzy_match(s1: str, s2: str) -> bool:
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s1 = normalize(s1)
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s2 = normalize(s2)
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if s1 == '' or s2 == '':
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return s1 == s2
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return s1 in s2 or s2 in s1
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@LOAD_DATASET.register_module()
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class DropOpenAIDataset(BaseDataset):
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@staticmethod
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def load(path):
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path = get_data_path(path, local_mode=True)
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dataset_list = []
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with open(path, 'r', encoding='utf-8') as f:
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for line in f:
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data = json.loads(line)
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item = {
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'prompt': data['context'],
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'answers': data['ref_text'],
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}
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dataset_list.append(item)
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dataset_list = Dataset.from_list(dataset_list)
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return DatasetDict({'validation': dataset_list})
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class DropOpenAIEvaluator(BaseEvaluator):
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def score(self, predictions, references):
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if len(predictions) != len(references):
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return {'error': 'preds and refers have different length'}
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num_correct = 0
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count = 0
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details = []
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for pred, refr in zip(predictions, references):
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match = re.search(ANSWER_PATTERN, pred)
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extracted_answer = match.group(1) if match else pred
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refrs = refr.split('|')
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matches = [
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fuzzy_match(extracted_answer, correct_answer)
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for correct_answer in refrs
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]
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correct = True in matches
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num_correct += correct
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detail = {'pred': pred, 'answer': refr, 'correct': correct}
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count += 1
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details.append(detail)
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result = {'accuracy': 100 * num_correct / count, 'details': details}
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return result
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