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50 lines
1.7 KiB
Python
50 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.datasets import CMBDataset
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.utils.text_postprocessors import multiple_select_postprocess
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cmb_datasets = []
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for split in ['val', 'test']:
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cmb_reader_cfg = dict(
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input_columns=['exam_type', 'exam_class', 'question_type', 'question', 'option_str'],
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output_column='answer',
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train_split=split,
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test_split=split,
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)
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cmb_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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round=[
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dict(
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role='HUMAN',
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prompt=f'以下是中国{{exam_type}}中{{exam_class}}考试的一道{{question_type}}，不需要做任何分析和解释，直接输出答案选项。\n{{question}}\n{{option_str}} \n 答案: ',
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),
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dict(role='BOT', prompt='{answer}'),
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],
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),
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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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cmb_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type=multiple_select_postprocess),
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)
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cmb_datasets.append(
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dict(
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abbr='cmb' if split == 'val' else 'cmb_test',
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type=CMBDataset,
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path='./data/CMB/',
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reader_cfg=cmb_reader_cfg,
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infer_cfg=cmb_infer_cfg,
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eval_cfg=cmb_eval_cfg,
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)
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)
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