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from ..utils.function_utils import multi_choice_judge |
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""" |
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multi-choice single-label selection |
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metric: accuracy |
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争议焦点：识别案件涉及的争议焦点 |
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""" |
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def compute_jdzy(data_dict): |
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""" |
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Compute the Accuracy |
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The JEC dataset has 16 possible answers for each question, stored in the option_list |
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A prediction is correct if |
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1. The correct answer appears in the prediction, and |
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2. Options other than the answer do not appear in the prediction. |
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""" |
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score_list, abstentions = [], 0 |
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option_list = ["诉讼主体", "租金情况", "利息", "本金争议", "责任认定", "责任划分", "损失认定及处理", |
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"原审判决是否适当", "合同效力", "财产分割", "责任承担", "鉴定结论采信问题", "诉讼时效", "违约", "合同解除", "肇事逃逸"] |
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for example in data_dict: |
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question, prediction, answer = example["origin_prompt"], example["prediction"], example["refr"] |
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if answer[7:-1] == "赔偿": |
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continue |
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assert answer.startswith("争议焦点类别：") and answer[7:-1] in option_list, \ |
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f"answer: {answer} \n question: {question}" |
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answer_letter = answer[7:-1] |
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judge = multi_choice_judge(prediction, option_list, answer_letter) |
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score_list.append(judge["score"]) |
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abstentions += judge["abstention"] |
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accuracy = sum(score_list) / len(score_list) |
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return {"score": accuracy, "abstention_rate": abstentions / len(data_dict)} |
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