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50 lines
1.7 KiB
Python
50 lines
1.7 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.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import winograndeDataset_V2
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from opencompass.utils.text_postprocessors import first_option_postprocess
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winogrande_reader_cfg = dict(
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input_columns=['opt1', 'opt2'],
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output_column='answer',
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)
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winogrande_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
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)
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_winogrande_prompt = dict(
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prompt_1='Which of the following is a good sentence:\nA. {opt1}\nB. {opt2}\nAnswer:',
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prompt_2='Which is a good sentence out of the following:\nA. {opt1}\nB. {opt2}\nAnswer:',
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prompt_3='Can you identify a good sentence from the following:\nA. {opt1}\nB. {opt2}\nAnswer:',
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)
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winogrande_datasets = []
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for _choice in _winogrande_prompt:
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winogrande_datasets.append(
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dict(
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abbr='winogrande_'+_choice,
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type=winograndeDataset_V2,
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path='./data/winogrande',
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reader_cfg=winogrande_reader_cfg,
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infer_cfg=dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt=_winogrande_prompt[_choice]
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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),
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),
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eval_cfg=winogrande_eval_cfg),
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)
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del _choice
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