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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>
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
import re
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from datasets import Dataset
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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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@LOAD_DATASET.register_module()
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class MGSMSDataset(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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src_lines = open(path, 'r', encoding='utf-8').readlines()
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data = {'question': [], 'answer': []}
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for lines in src_lines:
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question, answer = lines.strip().split('\t')
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data['question'].append(question)
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data['answer'].append(answer)
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dataset = Dataset.from_dict({
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'question': data['question'],
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'answer': data['answer']
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})
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return dataset
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LANG_TO_ANSWER_PREFIX = {
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'en': 'Answer',
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'bn': 'উত্তর',
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'de': 'Antwort',
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'es': 'Respuesta',
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'fr': 'Réponse',
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'ja': '答え',
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'ru': 'Ответ',
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'sw': 'Jibu',
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'te': 'సమాధానం',
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'th': 'คำตอบ',
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'zh': '答案',
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}
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def mgsm_postprocess(text: str, lang: str) -> str:
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answer_prefix = LANG_TO_ANSWER_PREFIX[lang]
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if answer_prefix not in text:
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return ''
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answer_text = text.split(answer_prefix)[-1].strip()
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numbers = re.findall(r'\d+\.?\d*', answer_text.replace(',', ''))
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return numbers[-1].rstrip('.') if numbers else ''
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class MGSM_Evaluator(BaseEvaluator):
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def score(self, predictions, references):
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assert len(predictions) == len(references)
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num_correct, total = 0, 0
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details = {}
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for index, (references_answer, predictions_answer) in enumerate(
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zip(references, predictions)):
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if references_answer == predictions_answer:
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is_correct = True
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else:
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is_correct = False
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num_correct += is_correct
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total += 1
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details[str(index)] = {
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'references': references_answer,
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'predictions': predictions_answer,
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'correct': is_correct,
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}
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accuracy = num_correct / total * 100
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final_result = {'accuracy': accuracy, 'details': details}
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return final_result
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