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49 lines
1.7 KiB
Python
49 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 PPLInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import TNewsDataset
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tnews_reader_cfg = dict(input_columns='sentence', output_column='label_desc2')
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tnews_labels = [
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'农业新闻', '旅游新闻', '游戏新闻', '科技类别公司新闻', '体育类别新闻', '初升高教育新闻', '娱乐圈新闻', '投资资讯',
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'军事类别常识', '车辆新闻', '楼市新闻', '环球不含中国类别新闻', '书籍文化历史类别新闻', '故事类别新闻', '股票市场类别新闻'
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]
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tnews_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template={
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lb: dict(round=[
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dict(role='HUMAN', prompt='以下内容属于什么新闻：{sentence}。'),
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dict(role='BOT', prompt=lb)
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])
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for lb in tnews_labels
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}),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=PPLInferencer))
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tnews_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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tnews_datasets = [
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dict(
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type=TNewsDataset,
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path='json',
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abbr='tnews-dev',
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data_files='./data/FewCLUE/tnews/dev_few_all.json',
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split='train',
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reader_cfg=tnews_reader_cfg,
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infer_cfg=tnews_infer_cfg,
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eval_cfg=tnews_eval_cfg),
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dict(
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type=TNewsDataset,
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path='json',
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abbr='tnews-test',
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data_files='./data/FewCLUE/tnews/test_public.json',
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split='train',
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reader_cfg=tnews_reader_cfg,
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infer_cfg=tnews_infer_cfg,
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eval_cfg=tnews_eval_cfg)
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]
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