import os from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_inferencer import PPLInferencer from opencompass.datasets import CharmDataset from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator charm_tasks = [ ['Chinese_Anachronisms_Judgment', 'AB'], ['Chinese_Movie_and_Music_Recommendation', 'ABCD'], ['Chinese_Natural_Language_Inference', 'ABC'], ['Chinese_Reading_Comprehension', 'ABCD'], ['Chinese_Sequence_Understanding', 'ABCD'], ['Chinese_Sport_Understanding', 'AB'], ['Chinese_Time_Understanding', 'ABCD'], ['Global_Anachronisms_Judgment', 'AB'], ['Global_Movie_and_Music_Recommendation', 'ABCD'], ['Global_Natural_Language_Inference', 'ABC'], ['Global_Reading_Comprehension', 'ABCD'], ['Global_Sequence_Understanding', 'ABCD'], ['Global_Sport_Understanding', 'AB'], ['Global_Time_Understanding', 'ABCDEF'], ] charm_reason_datasets = [] for task_name, options in charm_tasks: with open(os.path.join(os.path.dirname(__file__), 'few-shot-examples', f'{task_name}_Direct.txt'), 'r') as f: few_shot_example = f.read() charm_reason_reader_cfg = dict(input_columns=['input'], output_column='target') charm_reason_infer_cfg = dict( prompt_template=dict( type=PromptTemplate, template={ f'({opt})': f'{few_shot_example}\n{{input}}\nA: {opt}' for opt in options }, ), retriever=dict(type=ZeroRetriever), inferencer=dict(type=PPLInferencer), ) charm_reason_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator)) charm_reason_datasets.append( dict( type=CharmDataset, abbr=f'charm-reason-{task_name}_Direct', path=f'data/CHARM/reasoning', name=task_name, reader_cfg=charm_reason_reader_cfg, infer_cfg=charm_reason_infer_cfg, eval_cfg=charm_reason_eval_cfg, ) )