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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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