File size: 1,586 Bytes
256a159 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
from ..utils.function_utils import multi_choice_judge
"""
multi-choice single-label selection
metric: accuracy
争议焦点：识别案件涉及的争议焦点
"""
def compute_jdzy(data_dict):
"""
Compute the Accuracy
The JEC dataset has 16 possible answers for each question, stored in the option_list
A prediction is correct if
1. The correct answer appears in the prediction, and
2. Options other than the answer do not appear in the prediction.
"""
score_list, abstentions = [], 0
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",
"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"]
for example in data_dict:
question, prediction, answer = example["origin_prompt"], example["prediction"], example["refr"]
if answer[7:-1] == "赔偿":
# todo: dataset imperfection
continue
assert answer.startswith("争议焦点类别：") and answer[7:-1] in option_list, \
f"answer: {answer} \n question: {question}"
answer_letter = answer[7:-1]
judge = multi_choice_judge(prediction, option_list, answer_letter)
score_list.append(judge["score"])
abstentions += judge["abstention"]
# compute the accuracy of score_list
accuracy = sum(score_list) / len(score_list)
return {"score": accuracy, "abstention_rate": abstentions / len(data_dict)}
|