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* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
50 lines
1.4 KiB
Python
50 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 PPLInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import WSCDatasetV3
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WSC_reader_cfg = dict(
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input_columns=['span1', 'span2', 'text'],
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output_column='label',
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)
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WSC_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template={
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'A':
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dict(round=[
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dict(
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role='HUMAN',
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prompt='Passage: {text}\nDoes the pronoun # {span2} # refer to * {span1} *?\nA. Yes\nB. No\nAnswer: '
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),
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dict(role='BOT', prompt='A'),
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]),
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'B':
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dict(round=[
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dict(
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role='HUMAN',
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prompt='Passage: {text}\nDoes the pronoun # {span2} # refer to * {span1} *?\nA. Yes\nB. No\nAnswer: '
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),
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dict(role='BOT', prompt='B'),
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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=PPLInferencer),
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)
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WSC_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
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WSC_datasets = [
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dict(
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abbr='WSC',
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type=WSCDatasetV3,
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path='./data/SuperGLUE/WSC/val.jsonl',
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reader_cfg=WSC_reader_cfg,
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infer_cfg=WSC_infer_cfg,
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eval_cfg=WSC_eval_cfg,
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)
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]
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