OpenCompass/configs/datasets/glm/mmlu.py

from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import GLMChoiceInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import HFDataset

mmlu_reader_cfg = dict(
    input_columns=['input', 'A', 'B', 'C', 'D'],
    output_column='target',
    train_split='validation')

mmlu_prompt_template = dict(
    type=PromptTemplate,
    template=None,
    column_token_map={
        'input': '</input>',
        'A': '</A>',
        'B': '</B>',
        'C': '</C>',
        'D': '</D>',
        'target': '</target>'
    },
    ice_token='</E>',
)

mmlu_infer_cfg = dict(
    ice_template=dict(
        type=PromptTemplate,
        template={
            target: '</input>\n(A) </A>\n(B) </B>\n(C) </C>\n(D) </D>\n'
            f'Answer: ({target}) </{target}>\n'
            for target in ['A', 'B', 'C', 'D']
        },
        column_token_map={
            'input': '</input>',
            'A': '</A>',
            'B': '</B>',
            'C': '</C>',
            'D': '</D>',
            'target': '</target>'
        }),
    prompt_template=mmlu_prompt_template,
    retriever=dict(type=FixKRetriever),
    inferencer=dict(type=GLMChoiceInferencer, fix_id_list=[0, 1, 2, 3, 4]))

mmlu_eval_cfg = dict(evaluator=dict(type=AccEvaluator))

mmlu_all_sets = [
    "college_biology",
    #  "college_chemistry",
    #  "college_computer_science",
    #  "college_mathematics",
    #  "college_physics",
    #  "electrical_engineering",
    #  "astronomy",
    #  "anatomy",
    #  "abstract_algebra",
    #  "machine_learning",
    #  "clinical_knowledge",
    #  "global_facts",
    #  "management",
    #  "nutrition",
    #  "marketing",
    #  "professional_accounting",
    #  "high_school_geography",
    #  "international_law",
    #  "moral_scenarios",
    #  "computer_security",
    #  "high_school_microeconomics",
    #  "professional_law",
    #  "medical_genetics",
    #  "professional_psychology",
    #  "jurisprudence",
    #  "world_religions",
    #  "philosophy",
    #  "virology",
    #  "high_school_chemistry",
    #  "public_relations",
    #  "high_school_macroeconomics",
    #  "human_sexuality",
    #  "elementary_mathematics",
    #  "high_school_physics",
    #  "high_school_computer_science",
    #  "high_school_european_history",
    #  "business_ethics",
    #  "moral_disputes",
    #  "high_school_statistics",
    #  "miscellaneous",
    #  "formal_logic",
    #  "high_school_government_and_politics",
    #  "prehistory",
    #  "security_studies",
    #  "high_school_biology",
    #  "logical_fallacies",
    #  "high_school_world_history",
    #  "professional_medicine",
    #  "high_school_mathematics",
    #  "college_medicine",
    #  "high_school_us_history",
    #  "sociology",
    #  "econometrics",
    #  "high_school_psychology",
    #  "human_aging",
    #  "us_foreign_policy",
    #  "conceptual_physics",
]

mmlu_key_sets = [
    'college_biology',
    'college_chemistry',
    'college_computer_science',
    'college_mathematics',
    'college_physics',
    'electrical_engineering',
    'astronomy',
    'anatomy',
    'abstract_algebra',
    'machine_learning',
    'clinical_knowledge',
    'global_facts',
    'management',
    'nutrition',
    'marketing',
    'professional_accounting',
]

mmlu_datasets = []
for name in mmlu_all_sets:
    mmlu_datasets.append(
        dict(
            type=HFDataset,
            path='lukaemon/mmlu',
            name=name,
            reader_cfg=mmlu_reader_cfg,
            infer_cfg=mmlu_infer_cfg.copy(),
            eval_cfg=mmlu_eval_cfg))
    mmlu_datasets[-1]['infer_cfg'][
        'prompt_template'] = mmlu_prompt_template.copy()
    mmlu_datasets[-1]['infer_cfg']['prompt_template']['template'] = dict(
        begin=[
            dict(
                role='SYSTEM',
                fallback_role='HUMAN',
                prompt=
                f'The following are multiple choice questions (with answers) about {name.replace("_", " ")}.'
            ),
            '</E>',
        ],
        round=[
            dict(
                role='HUMAN',
                prompt=
                '</input>\n(A) </A>\n(B) </B>\n(C) </C>\n(D) </D>\nAnswer: ('),
        ],
    )