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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
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import FixKRetriever
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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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_hint = 'The following are text classification questions. \n' \
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'Please determine whether the following sentence is linguistically acceptable: ' \
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'0 means unacceptable, 1 means acceptable.\n'
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CoLA_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template='Sentence: {sentence}\nResult: {label}',
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),
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prompt_template=dict(
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type=PromptTemplate,
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template={
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answer:
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f'{_hint}</E>Sentence: {{sentence}}\nResult: {answer}'
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for answer in [0, 1]
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},
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ice_token='</E>',
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),
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retriever=dict(type=FixKRetriever, fix_id_list=[17, 18, 19, 20, 21]),
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inferencer=dict(type=PPLInferencer))
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CoLA_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
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CoLA_datasets = []
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for _split in ['validation']:
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CoLA_reader_cfg = dict(
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input_columns=['sentence'],
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output_column='label',
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test_split=_split
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)
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CoLA_datasets.append(
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dict(
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abbr=f'CoLA-{_split}',
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type=HFDataset,
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path='glue',
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name='cola',
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reader_cfg=CoLA_reader_cfg,
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infer_cfg=CoLA_infer_cfg,
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eval_cfg=CoLA_eval_cfg
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)
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)
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