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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
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 HuProverbDataset2CQ, HuProverb_Evaluator_2CQ
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with read_base():
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from .HuProverbRea_setting import INSTRUCTIONS_DIRECT_QA, DATA_PATH, DATA_VERSION
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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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HuProverbRea_reader_cfg = dict(
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input_columns=[
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'hu_text', 'context', 'en_expl', 'hu_expl', 'option1', 'option2'
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],
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output_column='out',
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)
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HuProverbRea_datasets = []
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instruction = INSTRUCTIONS_DIRECT_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(role='HUMAN', prompt=instruction),
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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_2CQ))
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HuProverbRea_datasets.append(
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dict(
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abbr=
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f'OpenHuEval_HuProverbRea_{DATA_VERSION}_2CQ-prompt_{prompt_template_language}',
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type=HuProverbDataset2CQ,
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filepath=DATA_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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