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75 lines
2.4 KiB
Python
75 lines
2.4 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 AccEvaluator
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from opencompass.datasets import TNewsDataset_V2
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tnews_reader_cfg = dict(
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input_columns="sentence",
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output_column="label_desc2",
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)
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tnews_labels = [
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"农业新闻", # news_agriculture
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"旅游新闻", # news_travel
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"游戏新闻", # news_game
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"科技类别公司新闻", # news_tech
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"体育类别新闻", # news_sports
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"初升高教育新闻", # news_edu
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"娱乐圈新闻", # news_entertainment
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"投资资讯", # news_finance
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"军事类别常识", # news_military
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"车辆新闻", # news_car
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"楼市新闻", # news_house
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"环球不含中国类别新闻", # news_world
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"书籍文化历史类别新闻", # news_culture
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"故事类别新闻", # news_story
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"股票市场类别新闻", # news_stock
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]
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_tnews_options_list_str = "\n".join(f'{chr(ord("A") + i)}. {tnews_labels[i]}'
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for i in range(len(tnews_labels)))
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_tnews_options_range_str = "，".join(f'“{chr(ord("A") + i)}”'
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for i in range(len(tnews_labels)))
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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=dict(round=[
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dict(
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role="HUMAN",
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prompt=
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f"{{sentence}}\n请判断上述内容属于什么新闻？\n{_tnews_options_list_str}\n请从{_tnews_options_range_str}中进行选择。\n答：",
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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),
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)
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tnews_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_role="BOT",
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pred_postprocessor=dict(type="first-capital"),
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)
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tnews_datasets = [
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dict(
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abbr="tnews-dev",
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type=TNewsDataset_V2,
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path="./data/FewCLUE/tnews/dev_few_all.json",
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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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dict(
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abbr="tnews-test",
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type=TNewsDataset_V2,
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path="./data/FewCLUE/tnews/test_public.json",
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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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]
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del _tnews_options_list_str, _tnews_options_range_str
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