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import os |
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import datasets |
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import pandas as pd |
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_CITATION = """\ |
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@article{huang2023ceval, |
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title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, |
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author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, |
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journal={arXiv preprint arXiv:2305.08322}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. |
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""" |
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_HOMEPAGE = "https://cevalbenchmark.com" |
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_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" |
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_URL = r"https://huggingface.co/datasets/ceval/ceval-exam/resolve/main/ceval-exam.zip" |
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task_list = [ |
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"computer_network", |
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"operating_system", |
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"computer_architecture", |
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"college_programming", |
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"college_physics", |
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"college_chemistry", |
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"advanced_mathematics", |
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"probability_and_statistics", |
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"discrete_mathematics", |
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"electrical_engineer", |
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"metrology_engineer", |
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"high_school_mathematics", |
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"high_school_physics", |
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"high_school_chemistry", |
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"high_school_biology", |
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"middle_school_mathematics", |
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"middle_school_biology", |
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"middle_school_physics", |
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"middle_school_chemistry", |
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"veterinary_medicine", |
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"college_economics", |
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"business_administration", |
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"marxism", |
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"mao_zedong_thought", |
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"education_science", |
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"teacher_qualification", |
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"high_school_politics", |
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"high_school_geography", |
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"middle_school_politics", |
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"middle_school_geography", |
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"modern_chinese_history", |
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"ideological_and_moral_cultivation", |
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"logic", |
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"law", |
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"chinese_language_and_literature", |
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"art_studies", |
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"professional_tour_guide", |
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"legal_professional", |
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"high_school_chinese", |
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"high_school_history", |
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"middle_school_history", |
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"civil_servant", |
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"sports_science", |
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"plant_protection", |
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"basic_medicine", |
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"clinical_medicine", |
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"urban_and_rural_planner", |
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"accountant", |
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"fire_engineer", |
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"environmental_impact_assessment_engineer", |
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"tax_accountant", |
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"physician", |
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] |
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class CevalExamConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
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class CevalExam(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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CevalExamConfig( |
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name=task_name, |
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) |
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for task_name in task_list |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"id":datasets.Value("int32"), |
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"question": datasets.Value("string"), |
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"A": datasets.Value("string"), |
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"B": datasets.Value("string"), |
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"C": datasets.Value("string"), |
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"D": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"explanation":datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(_URL) |
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task_name = self.config.name |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, "test", f"{task_name}_test.csv" |
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), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("val"), |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, "val", f"{task_name}_val.csv" |
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), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split("dev"), |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, "dev", f"{task_name}_dev.csv" |
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), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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df = pd.read_csv(filepath,encoding="utf-8") |
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for i, instance in enumerate(df.to_dict(orient="records")): |
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if "answer" not in instance.keys(): |
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instance["answer"]="" |
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if "explanation" not in instance.keys(): |
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instance["explanation"]="" |
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yield i, instance |
