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46 lines
1.8 KiB
Python
46 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 GenInferencer
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from opencompass.openicl.icl_evaluator import EMEvaluator, RougeEvaluator, SquadEvaluator, AccEvaluator
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from opencompass.datasets.leval import LEvalCourseraDataset
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from opencompass.utils.text_postprocessors import first_capital_postprocess, first_capital_postprocess_multi
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LEval_coursera_reader_cfg = dict(
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input_columns=['context', 'question'],
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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_coursera_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 based on this document. For multi-choice questions, there could be a single correct option or multiple correct options. Please only provide the letter corresponding to the answer (like A or AB) when answering.'),
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],
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round=[
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dict(role='HUMAN', prompt='Document is as follows.\n{context}\nQuestion:{question}\nAnswer:'),
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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=10)
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)
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LEval_coursera_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type=first_capital_postprocess_multi),
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pred_role='BOT'
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)
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LEval_coursera_datasets = [
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dict(
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type=LEvalCourseraDataset,
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abbr='LEval_coursera',
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path='L4NLP/LEval',
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name='coursera',
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reader_cfg=LEval_coursera_reader_cfg,
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infer_cfg=LEval_coursera_infer_cfg,
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eval_cfg=LEval_coursera_eval_cfg)
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]
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