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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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from mmengine.config import read_base
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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.datasets.OpenHuEval.HuProverbRea import HuProverbDatasetOE, HuProverb_Evaluator_OE
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with read_base():
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from .prompts import INSTRUCTIONS_OE_DIR_QA
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# currently we use English prompts with hu proverbs inserted
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prompt_template_language = 'en'
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dataset_path = '/mnt/hwfile/opendatalab/gaojunyuan/shared_data/OpenHuEval/data/HuProverbRea/HuProverbRea_250127'
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HuProverbRea_reader_cfg = dict(input_columns=['hu_text', 'context', 'en_expl', 'hu_expl', 'option1', 'option2'],
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output_column='out')
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HuProverbRea_datasets = []
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instruction = INSTRUCTIONS_OE_DIR_QA[prompt_template_language]
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HuProverbRea_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='</E>',
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round=[
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dict(
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role='HUMAN',
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prompt=instruction
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),
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],
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),
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ice_token='</E>',
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer),
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)
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HuProverbRea_eval_cfg = dict(evaluator=dict(type=HuProverb_Evaluator_OE))
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HuProverbRea_datasets.append(
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dict(
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abbr=f'HuProverbRea_OE_{prompt_template_language}',
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type=HuProverbDatasetOE,
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path=dataset_path,
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reader_cfg=HuProverbRea_reader_cfg,
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infer_cfg=HuProverbRea_infer_cfg,
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eval_cfg=HuProverbRea_eval_cfg,
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)
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)
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