LLM_Evaluator/evaluators/evaluator.py

import re
import string
class Evaluator:
    def __init__(self, choices, model_name, k=-1):
        self.choices = choices
        self.model_name = model_name
        self.k = k
        self.puncs = list(string.punctuation)

    def format_example(self, line, include_answer=True):
        example = line['question']
        # print(example)
        for choice in self.choices:
            example += f'\n{choice}. {line[f"{choice}"]}'
        example += '\n答案：'
        if include_answer:
            example += f'{line["answer"]}\n\n'
        return example

    def generate_few_shot_prompt(self, subject, dev_df):
        prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
        k = self.k
        if self.k == -1:
            k = dev_df.shape[0]
        for i in range(k):
            prompt += self.format_example(dev_df.iloc[i, :])
        return prompt

    def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, save_result_dir=None):
        pass

    def eval_qa(self, subject_name, qa_df, save_result_dir=None):
        pass

    def normalize_answer(self,s):

        def white_space_fix(text):
            return ' '.join(text.split())

        def remove_punc(text):
            exclude=set(self.puncs)
            return ''.join(ch for ch in text if ch not in exclude)

        def lower(text):
            return text.lower()

        return white_space_fix(remove_punc(lower(s)))

    def exact_match(self,pred, target):
        return self.normalize_answer(pred)==self.normalize_answer(target)