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103 lines
2.7 KiB
Python
103 lines
2.7 KiB
Python
# flake8: noqa
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import json
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import os.path as osp
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from datasets import Dataset
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from opencompass.registry import LOAD_DATASET
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from ..base import BaseDataset
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base_prompt_zh = """请根据 用户问题 以及 相应的两个回答，判断哪一个回答更好。
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[用户问题]
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{question}
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[回答1开始]
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{prediction}
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[回答1结束]
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[回答2开始]
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{prediction2}
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[回答2结束]
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请先对两个回答进行评价，最后在以下 3 个选项中做出选择:
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A. 回答1更好
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B. 回答2更好
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C. 回答1、2平局
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如果你认为回答1更好，你的输出应形如：
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评价1：回答1 xxx
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评价2：回答2 xxx
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选择：[[A]]
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如果你认为回答2更好，你的输出应形如：
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评价1：回答1 xxx
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评价2：回答2 xxx
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选择：[[B]]
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如果你认为回答1、2打成平手，你的输出应形如：
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评价1：回答1 xxx
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评价2：回答2 xxx
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选择：[[C]]
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"""
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base_prompt_en = """Please evaluate the two responses based on the user's question and then choose from the following three options:
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A. Response 1 is better
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B. Response 2 is better
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C. Both responses are equal
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[user's question]
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{question}
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[Response 1 Start]
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{prediction}
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[Response 1 End]
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[Response 2 Start]
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{prediction2}
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[Response 2 End]
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If you believe that Response 1 is better, your output should be formatted as follows:
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Evaluation 1: Response 1 xxx
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Evaluation 2: Response 2 xxx
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Choice: [[A]]
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If you believe that Response 2 is better, your output should be formatted as follows:
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Evaluation 1: Response 1 xxx
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Evaluation 2: Response 2 xxx
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Choice: [[B]]
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If you believe that both responses are equally good, your output should be formatted as follows:
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Evaluation 1: Response 1 xxx
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Evaluation 2: Response 2 xxx
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Choice: [[C]]
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"""
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@LOAD_DATASET.register_module()
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class CompassBenchDataset(BaseDataset):
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def load(self, path: str, name: str):
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filename = osp.join(path, f'{name}.json')
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raw_data = []
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with open(filename, 'r', encoding='utf-8') as f:
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json_data = json.load(f)
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for problem in json_data:
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question = problem['question']
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lan = problem['language']
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others = problem['others']
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judge_prompt = base_prompt_zh if lan == 'zh' else base_prompt_en
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judge_prompt = judge_prompt.replace('{question}', question)
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raw_data.append({
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'question': question,
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'judge_prompt': judge_prompt,
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'judge': {
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'lan': lan,
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'level': others['level'],
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'category': problem['category'],
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'question': question
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}
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})
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dataset = Dataset.from_list(raw_data)
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return dataset
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