# Copyright (c) Alibaba, Inc. and its affiliates. import csv import os from collections import defaultdict from evalscope.benchmarks import Benchmark, DataAdapter from evalscope.constants import EvalType, OutputType from evalscope.metrics import exact_match from evalscope.metrics.completion_parsers import ResponseParser from evalscope.utils.io_utils import csv_to_list from evalscope.utils.logger import get_logger # flake8: noqa logger = get_logger() SUBSET_LIST = [ 'computer_network', 'operating_system', 'computer_architecture', 'college_programming', 'college_physics', 'college_chemistry', 'advanced_mathematics', 'probability_and_statistics', 'discrete_mathematics', 'electrical_engineer', 'metrology_engineer', 'high_school_mathematics', 'high_school_physics', 'high_school_chemistry', 'high_school_biology', 'middle_school_mathematics', 'middle_school_biology', 'middle_school_physics', 'middle_school_chemistry', 'veterinary_medicine', 'college_economics', 'business_administration', 'marxism', 'mao_zedong_thought', 'education_science', 'teacher_qualification', 'high_school_politics', 'high_school_geography', 'middle_school_politics', 'middle_school_geography', 'modern_chinese_history', 'ideological_and_moral_cultivation', 'logic', 'law', 'chinese_language_and_literature', 'art_studies', 'professional_tour_guide', 'legal_professional', 'high_school_chinese', 'high_school_history', 'middle_school_history', 'civil_servant', 'sports_science', 'plant_protection', 'basic_medicine', 'clinical_medicine', 'urban_and_rural_planner', 'accountant', 'fire_engineer', 'environmental_impact_assessment_engineer', 'tax_accountant', 'physician', ] SUBJECT_MAPPING = { 'computer_network': ['Computer Network', '计算机网络', 'STEM'], 'operating_system': ['Operating System', '操作系统', 'STEM'], 'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'], 'college_programming': ['College Programming', '大学编程', 'STEM'], 'college_physics': ['College Physics', '大学物理', 'STEM'], 'college_chemistry': ['College Chemistry', '大学化学', 'STEM'], 'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'], 'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'], 'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'], 'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'], 'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'], 'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'], 'high_school_physics': ['High School Physics', '高中物理', 'STEM'], 'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'], 'high_school_biology': ['High School Biology', '高中生物', 'STEM'], 'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'], 'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'], 'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'], 'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'], 'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'], 'college_economics': ['College Economics', '大学经济学', 'Social Science'], 'business_administration': ['Business Administration', '工商管理', 'Social Science'], 'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'], 'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'], 'education_science': ['Education Science', '教育学', 'Social Science'], 'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'], 'high_school_politics': ['High School Politics', '高中政治', 'Social Science'], 'high_school_geography': ['High School Geography', '高中地理', 'Social Science'], 'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'], 'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'], 'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'], 'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'], 'logic': ['Logic', '逻辑学', 'Humanities'], 'law': ['Law', '法学', 'Humanities'], 'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'], 'art_studies': ['Art Studies', '艺术学', 'Humanities'], 'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'], 'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'], 'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'], 'high_school_history': ['High School History', '高中历史', 'Humanities'], 'middle_school_history': ['Middle School History', '初中历史', 'Humanities'], 'civil_servant': ['Civil Servant', '公务员', 'Other'], 'sports_science': ['Sports Science', '体育学', 'Other'], 'plant_protection': ['Plant Protection', '植物保护', 'Other'], 'basic_medicine': ['Basic Medicine', '基础医学', 'Other'], 'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'], 'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'], 'accountant': ['Accountant', '注册会计师', 'Other'], 'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'], 'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'], 'tax_accountant': ['Tax Accountant', '税务师', 'Other'], 'physician': ['Physician', '医师资格', 'Other'] } @Benchmark.register( name='ceval', pretty_name='C-Eval', tags=['Knowledge', 'MCQ', 'Chinese'], description= 'C-Eval is a benchmark designed to evaluate the performance of AI models on Chinese exams across various subjects, including STEM, social sciences, and humanities. It consists of multiple-choice questions that test knowledge and reasoning abilities in these areas.', # noqa: E501 dataset_id='modelscope/ceval-exam', model_adapter=OutputType.GENERATION, output_types=[OutputType.MULTIPLE_CHOICE, OutputType.GENERATION], subset_list=SUBSET_LIST, metric_list=['AverageAccuracy'], few_shot_num=0, train_split='dev', eval_split='val', prompt_template= '以下是中国关于{subset_name}考试的单项选择题，请选出其中的正确答案。你的回答的最后一行应该是这样的格式：“答案是：LETTER”（不带引号），其中 LETTER 是 A、B、C、D 中的一个。\n{query}', ) class CEVALAdapter(DataAdapter): def __init__(self, **kwargs): few_shot_num = kwargs.get('few_shot_num', 0) if few_shot_num > 5: logger.warning(f'few_shot_num <= 5 for C-Eval, but got {few_shot_num}. Use 5-shot by default.') kwargs['few_shot_num'] = 5 super().__init__(**kwargs) self.category_map = {k: v[-1] for k, v in SUBJECT_MAPPING.items()} self.choices = ['A', 'B', 'C', 'D'] def load_from_disk(self, dataset_name_or_path, subset_list, work_dir, **kwargs) -> dict: data_dict = defaultdict(dict) for subset_name in subset_list: for split_name in [self.train_split, self.eval_split]: if os.path.exists(dataset_name_or_path): file_path = os.path.join(dataset_name_or_path, f'{subset_name}_{split_name}.csv') else: file_path = os.path.join(work_dir, dataset_name_or_path, f'{subset_name}_{split_name}.csv') if os.path.exists(file_path): data_dict[subset_name][split_name] = csv_to_list(file_path) return data_dict def gen_prompt(self, input_d: dict, subset_name: str, few_shot_list: list, **kwargs) -> dict: """ Generate model prompt from raw input, unify the prompt format for C-Eval benchmark. Args: input_d (dict): The raw input. A single data format of the C-Eval: {'id': 0, 'question': '下列关于税法基本原则的表述中，不正确的是____。', 'A': '税收法定原则包括税收要件法定原则和税务合法性原则', 'B': '税收公平原则源于法律上的平等性原则', 'C': '税收效率原则包含经济效率和行政效率两个方面', 'D': '税务机关按法定程序依法征税，可以自由做出减征、停征或免征税款的决定', 'answer': 'D', 'explanation': ''} Returns: {'data': ['prompt ...']} """ few_shot_prompts = [self._format_example(input_d=sample, include_answer=True) for sample in few_shot_list] if len(few_shot_prompts) > 0: context: str = '\n'.join(few_shot_prompts) + '\n' else: context = '' query: str = context.strip() + self._format_example(input_d=input_d, include_answer=False) subject_name: str = SUBJECT_MAPPING.get(subset_name)[1] if SUBJECT_MAPPING.get(subset_name) else subset_name full_prompt = self.prompt_template.format(subset_name=subject_name, query=query) return self.gen_prompt_data(full_prompt) def get_gold_answer(self, input_d: dict) -> str: # Get the gold choice return input_d.get('answer', '') def parse_pred_result(self, result: str, raw_input_d: dict = None, eval_type: str = EvalType.CHECKPOINT) -> str: """ Parse the model output to get the answer. Could be the best choice index. Args: result: Predicted answer from the model. Usually a string for chat. raw_input_d (dict): The raw input. Depending on the dataset. eval_type: `checkpoint` or `service` or `custom`. Default is `checkpoint`. Returns: The parsed answer. Depending on the dataset. Usually a string for chat. """ if self.model_adapter == OutputType.MULTIPLE_CHOICE: return result else: return ResponseParser.parse_first_option_with_choices(text=result, options=self.choices) def match(self, gold: str, pred: str) -> float: return exact_match(gold=gold, pred=pred) def _format_example(self, input_d: dict, include_answer=True): example = '问题：' + input_d['question'] for choice in self.choices: example += f'\n{choice}. {input_d[f"{choice}"]}' if include_answer: example += '\n答案: ' + input_d['answer'] + '\n\n' else: example += '\n答案: ' return example