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63 lines
1.8 KiB
Python
63 lines
1.8 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 HFDataset
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CB_reader_cfg = dict(
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input_columns=['premise', 'hypothesis'],
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output_column='label',
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)
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CB_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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'contradiction':
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dict(round=[
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dict(
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role='HUMAN',
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prompt=
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'{premise}\n{hypothesis}\nWhat is the relation between the two sentences?'
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),
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dict(role='BOT', prompt='Contradiction'),
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]),
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'entailment':
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dict(round=[
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dict(
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role='HUMAN',
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prompt=
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'{premise}\n{hypothesis}\nWhat is the relation between the two sentences?'
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),
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dict(role='BOT', prompt='Entailment'),
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]),
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'neutral':
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dict(round=[
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dict(
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role='HUMAN',
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prompt=
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'{premise}\n{hypothesis}\nWhat is the relation between the two sentences?'
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),
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dict(role='BOT', prompt='Neutral'),
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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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CB_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
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CB_datasets = [
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dict(
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type=HFDataset,
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abbr='CB',
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path='json',
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split='train',
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data_files='./data/SuperGLUE/CB/val.jsonl',
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reader_cfg=CB_reader_cfg,
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infer_cfg=CB_infer_cfg,
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eval_cfg=CB_eval_cfg,
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)
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]
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