44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
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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 EMEvaluator, RougeEvaluator, SquadEvaluator
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from opencompass.datasets.leval import LEvalGPTEvaluator, LEvalNaturalQuestionDataset
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LEval_nq_reader_cfg = dict(
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input_columns=['context', 'question', 'length'],
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output_column='answer',
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train_split='test',
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test_split='test'
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)
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LEval_nq_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin=[
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dict(role='SYSTEM', fallback_role='HUMAN', prompt='Now you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph.'),
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],
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round=[
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dict(role='HUMAN', prompt='Document is as follows. {context}\nInstruction: {question}?\nAnswer this question with {length} words.'),
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dict(role='BOT', prompt=''),
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], )),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=50)
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)
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LEval_nq_eval_cfg = dict(
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evaluator=dict(type=LEvalGPTEvaluator),
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pred_role='BOT'
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)
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LEval_nq_datasets = [
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dict(
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type=LEvalNaturalQuestionDataset,
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abbr='LEval_nq',
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path='L4NLP/LEval',
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name='natural_question',
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reader_cfg=LEval_nq_reader_cfg,
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infer_cfg=LEval_nq_infer_cfg,
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eval_cfg=LEval_nq_eval_cfg)
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]
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