from opencompass.openicl.icl_prompt_template import PromptTemplate from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_inferencer import GenInferencer from opencompass.datasets import LawBenchDataset names = [ ["1-1", "article_recitation"], ["1-2", "knowledge_question_answering"], ["2-1", "document_proofreading"], ["2-2", "dispute_focus_identification"], ["2-3", "marital_disputes_identification"], ["2-4", "issue_topic_identification"], ["2-5", "reading_comprehension"], ["2-6", "named_entity_recognition"], ["2-7", "opinion_summarization"], ["2-8", "argument_mining"], ["2-9", "event_detection"], ["2-10", "trigger_word_extraction"], ["3-1", "fact_based_article_prediction"], ["3-2", "scene_based_article_prediction"], ["3-3", "charge_prediction"], ["3-4", "prison_term_prediction_wo_article"], ["3-5", "prison_term_prediction_w_article"], ["3-6", "case_analysis"], ["3-7", "criminal_damages_calculation"], ["3-8", "consultation"], ] lawbench_datasets = [] for index, name in names: lawbench_reader_cfg = dict( input_columns=['instruction', 'question'], output_column='answer') lawbench_infer_cfg = dict( prompt_template=dict( type=PromptTemplate, template=dict( round=[ dict(role="HUMAN", prompt="{instruction}\n{question}"), ] ), ), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer, max_out_len=1024) ) lawbench_eval_cfg = dict( evaluator=dict(type='LawBenchEvaluator_' + index.replace('-', '_')) ) lawbench_datasets.append( dict( abbr='lawbench-' + index + '-' + name + '-0-shot', type=LawBenchDataset, path='./data/lawbench/zero_shot', index=index, reader_cfg=lawbench_reader_cfg, infer_cfg=lawbench_infer_cfg, eval_cfg=lawbench_eval_cfg ) )