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70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
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from opencompass.datasets import WildBenchDataset
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from opencompass.openicl.icl_evaluator import LMEvaluator
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from opencompass.openicl.icl_inferencer import ChatInferencer
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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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hu_life_qa_reader_cfg = dict(
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input_columns=["dialogue", "prompt"],
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output_column="judge",
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)
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data_path ="/mnt/hwfile/opendatalab/yanghaote/share/g13k_hu/g13k_hu_vpaper.jsonl"
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hu_life_qa_datasets = []
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hu_life_qa_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template="""{dialogue}"""
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(
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type=ChatInferencer,
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max_seq_len=4096,
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max_out_len=512,
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infer_mode="last",
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),
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)
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hu_life_qa_eval_cfg = dict(
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evaluator=dict(
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type=LMEvaluator,
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prompt_template=dict(
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type=PromptTemplate,
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template="""{prompt}"""
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),
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),
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pred_role="BOT",
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)
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hu_life_qa_datasets.append(
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dict(
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abbr="hu_life_qa",
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type=WildBenchDataset,
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path=data_path,
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reader_cfg=hu_life_qa_reader_cfg,
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infer_cfg=hu_life_qa_infer_cfg,
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eval_cfg=hu_life_qa_eval_cfg,
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)
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)
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task_group_new = {
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"business and finance": "business and finance",
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"childbearing and education": "life, culture, and customs",
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"culture and community": "life, culture, and customs",
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'culture and customs': "life, culture, and customs",
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"life, culture, and customs": "life, culture, and customs",
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"education and profession": "education and profession",
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"food and drink": "life, culture, and customs",
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"health": "life, culture, and customs",
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"holidays": "life, culture, and customs",
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"home": "life, culture, and customs",
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"person": "life, culture, and customs",
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"politics": "politics, policy and law",
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"politics, policy and law": "politics, policy and law",
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"public education and courses": "education and profession",
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"transport": "life, culture, and customs",
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"science": "life, culture, and customs",
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"travel": "life, culture, and customs",
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}
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