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115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
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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 AccContaminationEvaluator
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from opencompass.datasets import MMLUDatasetClean as MMLUDataset
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# None of the mmlu dataset in huggingface is correctly parsed, so we use our own dataset reader
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# Please download the dataset from https://people.eecs.berkeley.edu/~hendrycks/data.tar
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mmlu_reader_cfg = dict(
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input_columns=['input', 'A', 'B', 'C', 'D'],
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output_column='target',
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train_split='dev')
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mmlu_all_sets = [
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'college_biology',
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'college_chemistry',
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'college_computer_science',
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'college_mathematics',
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'college_physics',
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'electrical_engineering',
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'astronomy',
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'anatomy',
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'abstract_algebra',
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'machine_learning',
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'clinical_knowledge',
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'global_facts',
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'management',
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'nutrition',
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'marketing',
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'professional_accounting',
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'high_school_geography',
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'international_law',
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'moral_scenarios',
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'computer_security',
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'high_school_microeconomics',
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'professional_law',
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'medical_genetics',
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'professional_psychology',
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'jurisprudence',
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'world_religions',
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'philosophy',
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'virology',
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'high_school_chemistry',
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'public_relations',
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'high_school_macroeconomics',
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'human_sexuality',
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'elementary_mathematics',
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'high_school_physics',
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'high_school_computer_science',
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'high_school_european_history',
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'business_ethics',
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'moral_disputes',
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'high_school_statistics',
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'miscellaneous',
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'formal_logic',
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'high_school_government_and_politics',
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'prehistory',
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'security_studies',
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'high_school_biology',
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'logical_fallacies',
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'high_school_world_history',
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'professional_medicine',
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'high_school_mathematics',
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'college_medicine',
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'high_school_us_history',
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'sociology',
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'econometrics',
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'high_school_psychology',
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'human_aging',
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'us_foreign_policy',
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'conceptual_physics',
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]
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mmlu_datasets = []
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for _name in mmlu_all_sets:
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_hint = f'The following are multiple choice questions (with answers) about {_name.replace("_", " ")}.\n\n'
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mmlu_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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opt:
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f'{{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: {opt}\n'
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for opt in ['A', 'B', 'C', 'D']
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},
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),
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prompt_template=dict(
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type=PromptTemplate,
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template={
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opt:
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f'{_hint}</E>{{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: {opt}'
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for opt 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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mmlu_eval_cfg = dict(evaluator=dict(type=AccContaminationEvaluator),
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analyze_contamination=True)
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mmlu_datasets.append(
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dict(
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abbr=f'lukaemon_mmlu_{_name}',
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type=MMLUDataset,
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path='opencompass/mmlu',
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name=_name,
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reader_cfg=mmlu_reader_cfg,
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infer_cfg=mmlu_infer_cfg,
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eval_cfg=mmlu_eval_cfg,
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))
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del _name, _hint
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