from mmengine.config import read_base from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator from opencompass.datasets import MMLUCFDataset from opencompass.utils.text_postprocessors import first_option_postprocess with read_base(): from .mmlu_cf_categories import categories mmlu_cf_reader_cfg = dict( input_columns=['input', 'A', 'B', 'C', 'D'], output_column='target', train_split='dev') mmlu_cf_datasets = [] for _name in categories: _hint = f'There is a single choice question (with answers). Answer the question by replying A, B, C or D.' mmlu_cf_infer_cfg = dict( ice_template=dict( type=PromptTemplate, template=dict(round=[ dict( role='HUMAN', prompt= f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: ' ), dict(role='BOT', prompt='{target}\n') ]), ), prompt_template=dict( type=PromptTemplate, template=dict( begin='', round=[ dict( role='HUMAN', prompt=f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: ' ), ], ), ice_token='', ), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer), ) mmlu_cf_eval_cfg = dict( evaluator=dict(type=AccwithDetailsEvaluator), pred_postprocessor=dict(type=first_option_postprocess, options='ABCD')) mmlu_cf_datasets.append( dict( abbr=f'mmlu_cf_{_name}', type=MMLUCFDataset, path='microsoft/MMLU-CF', name=_name, reader_cfg=mmlu_cf_reader_cfg, infer_cfg=mmlu_cf_infer_cfg, eval_cfg=mmlu_cf_eval_cfg, )) del _name, _hint