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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
54 lines
1.5 KiB
Python
54 lines
1.5 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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RTE_reader_cfg = dict(
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input_columns=['hypothesis', 'premise'],
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output_column='label',
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test_split='train')
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RTE_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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'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}\nIs the sentence below entailed by the sentence above?'
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),
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dict(role='BOT', prompt='Yes'),
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]),
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'not_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}\nIs the sentence below entailed by the sentence above?'
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),
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dict(role='BOT', prompt='No'),
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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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RTE_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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RTE_datasets = [
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dict(
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type=HFDataset,
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abbr='RTE',
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path='json',
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data_files='./data/SuperGLUE/RTE/val.jsonl',
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split='train',
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reader_cfg=RTE_reader_cfg,
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infer_cfg=RTE_infer_cfg,
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eval_cfg=RTE_eval_cfg,
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)
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]
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