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from opencompass.openicl.icl_prompt_template import PromptTemplate |
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from opencompass.openicl.icl_retriever import FixKRetriever |
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from opencompass.openicl.icl_inferencer import PPLInferencer |
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from opencompass.openicl.icl_evaluator import AccEvaluator |
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from opencompass.datasets import CEvalDataset |
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ceval_subject_mapping = { |
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'computer_network': ['Computer Network', '计算机网络', 'STEM'], |
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'operating_system': ['Operating System', '操作系统', 'STEM'], |
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'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'], |
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'college_programming': ['College Programming', '大学编程', 'STEM'], |
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'college_physics': ['College Physics', '大学物理', 'STEM'], |
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'college_chemistry': ['College Chemistry', '大学化学', 'STEM'], |
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'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'], |
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'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'], |
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'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'], |
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'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'], |
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'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'], |
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'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'], |
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'high_school_physics': ['High School Physics', '高中物理', 'STEM'], |
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'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'], |
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'high_school_biology': ['High School Biology', '高中生物', 'STEM'], |
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'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'], |
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'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'], |
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'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'], |
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'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'], |
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'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'], |
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'college_economics': ['College Economics', '大学经济学', 'Social Science'], |
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'business_administration': ['Business Administration', '工商管理', 'Social Science'], |
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'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'], |
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'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'], |
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'education_science': ['Education Science', '教育学', 'Social Science'], |
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'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'], |
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'high_school_politics': ['High School Politics', '高中政治', 'Social Science'], |
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'high_school_geography': ['High School Geography', '高中地理', 'Social Science'], |
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'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'], |
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'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'], |
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'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'], |
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'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'], |
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'logic': ['Logic', '逻辑学', 'Humanities'], |
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'law': ['Law', '法学', 'Humanities'], |
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'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'], |
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'art_studies': ['Art Studies', '艺术学', 'Humanities'], |
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'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'], |
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'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'], |
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'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'], |
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'high_school_history': ['High School History', '高中历史', 'Humanities'], |
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'middle_school_history': ['Middle School History', '初中历史', 'Humanities'], |
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'civil_servant': ['Civil Servant', '公务员', 'Other'], |
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'sports_science': ['Sports Science', '体育学', 'Other'], |
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'plant_protection': ['Plant Protection', '植物保护', 'Other'], |
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'basic_medicine': ['Basic Medicine', '基础医学', 'Other'], |
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'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'], |
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'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'], |
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'accountant': ['Accountant', '注册会计师', 'Other'], |
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'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'], |
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'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'], |
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'tax_accountant': ['Tax Accountant', '税务师', 'Other'], |
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'physician': ['Physician', '医师资格', 'Other'], |
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} |
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ceval_all_sets = list(ceval_subject_mapping.keys()) |
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ceval_datasets = [] |
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for _split in ["val"]: |
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for _name in ceval_all_sets: |
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_ch_name = ceval_subject_mapping[_name][1] |
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ceval_infer_cfg = dict( |
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ice_template=dict( |
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type=PromptTemplate, |
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template={ |
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answer: dict( |
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begin="</E>", |
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round=[ |
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dict( |
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role="HUMAN", |
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prompt= |
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f"以下是中国关于{_ch_name}考试的单项选择题,请选出其中的正确答案。\n{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: " |
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), |
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dict(role="BOT", prompt=answer), |
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]) |
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for answer in ["A", "B", "C", "D"] |
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}, |
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ice_token="</E>", |
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), |
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retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]), |
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inferencer=dict(type=PPLInferencer), |
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) |
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ceval_eval_cfg = dict(evaluator=dict(type=AccEvaluator)) |
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ceval_datasets.append( |
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dict( |
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type=CEvalDataset, |
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path="./data/ceval/formal_ceval", |
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name=_name, |
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abbr="ceval-" + _name if _split == "val" else "ceval-test-" + |
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_name, |
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reader_cfg=dict( |
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input_columns=["question", "A", "B", "C", "D"], |
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output_column="answer", |
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train_split="dev", |
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test_split=_split), |
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infer_cfg=ceval_infer_cfg, |
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eval_cfg=ceval_eval_cfg, |
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)) |
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del _split, _name, _ch_name |
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