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47 lines
1.4 KiB
Python
47 lines
1.4 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 GLMChoiceInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import CslDataset
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csl_reader_cfg = dict(
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input_columns=["abst", "keywords"], output_column='label')
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csl_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template={
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0: "</E>摘要：</A>",
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1: "</E>摘要：</A>关键词：</K>"
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},
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column_token_map={
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"abst": '</A>',
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'keywords': '</K>'
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},
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ice_token='</E>'),
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prompt_template=dict(
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type=PromptTemplate,
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template=
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'</E>Abstract: </A>\nKeyword: </K>\n Does all keywords come from the given abstract? (Yes or No)',
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column_token_map={
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"abst": '</A>',
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'keywords': '</K>'
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},
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ice_token='</E>'),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GLMChoiceInferencer, choices=['No', 'Yes']))
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csl_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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csl_datasets = [
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dict(
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type=CslDataset,
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path='json',
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abbr='csl',
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data_files='./data/FewCLUE/csl/test_public.json',
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split='train',
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reader_cfg=csl_reader_cfg,
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infer_cfg=csl_infer_cfg,
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eval_cfg=csl_eval_cfg)
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]
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