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