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42 lines
1.7 KiB
Python
42 lines
1.7 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 JiebaRougeEvaluator
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from opencompass.datasets.rolebench import InstructionGeneralizationChineseDataset
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instruction_generalization_zh_reader_cfg = dict(
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input_columns=['role', 'desc', 'question'],
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output_column='answer',
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train_split='train',
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test_split='test'
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)
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instruction_generalization_zh_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='你是{role}，你的特征描述是：{desc}。现在请你回答我的一些问题，以准确展现你的人格特征！你的说话风格要全面模仿被赋予的人格角色！请不要暴露你是人工智能模型或者语言模型，你要时刻记住你只被赋予的一个人格角色。说话不要嗦，也不要太过于正式或礼貌。'),
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],
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round=[
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dict(role='HUMAN', prompt='{question}'),
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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=512)
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)
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instruction_generalization_zh_eval_cfg = dict(
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evaluator=dict(type=JiebaRougeEvaluator),
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pred_role='BOT'
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)
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instruction_generalization_zh_datasets = [
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dict(
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type=InstructionGeneralizationChineseDataset,
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path='ZenMoore/RoleBench',
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reader_cfg=instruction_generalization_zh_reader_cfg,
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infer_cfg=instruction_generalization_zh_infer_cfg,
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eval_cfg=instruction_generalization_zh_eval_cfg)
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]
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