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https://github.com/open-compass/opencompass.git
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1c95790fdd
* TabMWP * TabMWP * fixed * fixed * fixed * done * done * done * add new subjective judgement * add new subjective judgement * add new subjective judgement * add new subjective judgement * add new subjective judgement * modified to a more general way * modified to a more general way * final * final * add summarizer * add new summarize * fixed * fixed * fixed --------- Co-authored-by: caomaosong <caomaosong@pjlab.org.cn>
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
# flake8: noqa: E501
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import re
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from collections import defaultdict
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from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator
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from opencompass.registry import ICL_EVALUATORS
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def match_general_answer(s):
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temp = s[0]
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if temp in ['A', 'B', 'C', 'D']:
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return temp
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else:
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return None
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def match_GPT4_answer(s):
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if result := re.findall('(?:选择：|Choice: )([ABCD])', s):
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return result[0]
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else:
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return None
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@ICL_EVALUATORS.register_module()
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class Corev2Evaluator(BaseEvaluator):
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def __init__(self,
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base_model,
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compare_model,
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judge_method='gpt4',
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metric='win_rate'):
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self.base_model = base_model
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self.compare_model = compare_model
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self.metric = metric
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self.judge_method = judge_method
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def score(self, predictions, references):
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if self.judge_method == 'gpt4':
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predictions = [match_GPT4_answer(s) for s in predictions]
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else:
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predictions = [match_general_answer(s) for s in predictions]
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print(
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f'Among {len(predictions)} judgements, successfully extracted {len(predictions)-predictions.count(None)} judgements.'
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)
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win_both, half_draw, categories = defaultdict(float), defaultdict(
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float), defaultdict(float)
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for prediction, reference in zip(predictions, references):
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if prediction is not None:
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categories[reference['capability'].split('-')[0]] += 1
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winner = ''
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if prediction == 'A':
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winner = reference['model1']
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elif prediction == 'B':
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winner = reference['model2']
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elif prediction == 'C':
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win_both[reference['capability'].split('-')[0]] += 1
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if self.base_model == winner:
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half_draw[reference['capability'].split('-')[0]] += 1
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win_both[reference['capability'].split('-')[0]] += 1
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for capability in categories:
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if capability not in half_draw:
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win_both[capability] = 0.0
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half_draw[capability] = 0.0
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else:
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win_both[capability] = round(
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(win_both[capability] / categories[capability]) * 100, 2)
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half_draw[capability] = round(
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(half_draw[capability] / categories[capability]) * 100, 2)
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scores = {'win_both': win_both, 'half_draw': half_draw}
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return scores
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