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46cc7894e1
* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
59 lines
2.0 KiB
Python
59 lines
2.0 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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summedits_reader_cfg = dict(
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input_columns=['doc', 'summary'],
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output_column='label',
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test_split='train')
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summedits_prompt1 = "Given the document below, you have to determine if 'Yes' or 'No', the summary is factually consistent with the document."
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summedits_prompt2 = 'Document:\n{doc}\nSummary:\n{summary}\nIs the summary factually consistent with the document? '
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summedits_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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0:
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dict(
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begin=[
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dict(
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role='SYSTEM',
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fallback_role='HUMAN',
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prompt=summedits_prompt1)
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],
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round=[
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dict(role='HUMAN', prompt=summedits_prompt2),
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dict(role='BOT', prompt='No')
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]),
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1:
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dict(
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begin=[
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dict(
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role='SYSTEM',
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fallback_role='HUMAN',
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prompt=summedits_prompt1)
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],
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round=[
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dict(role='HUMAN', prompt=summedits_prompt2),
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dict(role='BOT', prompt='Yes')
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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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summedits_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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summedits_datasets = [
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dict(
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type=HFDataset,
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abbr='summedits',
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path='json',
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split='train',
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data_files='./data/summedits/summedits.jsonl',
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reader_cfg=summedits_reader_cfg,
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infer_cfg=summedits_infer_cfg,
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eval_cfg=summedits_eval_cfg)
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]
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