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77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import FixKRetriever
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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 FinanceIQDataset
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financeIQ_subject_mapping_en = {
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'certified_public_accountant': '注册会计师（CPA）',
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'banking_qualification': '银行从业资格',
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'securities_qualification': '证券从业资格',
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'fund_qualification': '基金从业资格',
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'insurance_qualification': '保险从业资格CICE',
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'economic_analyst': '经济师',
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'taxation_practitioner': '税务师',
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'futures_qualification': '期货从业资格',
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'certified_fin_planner': '理财规划师',
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'actuary_fin_math': '精算师-金融数学',
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}
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financeIQ_subject_mapping = {
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'注册会计师（CPA）': '注册会计师（CPA）',
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'银行从业资格': '银行从业资格',
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'证券从业资格': '证券从业资格',
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'基金从业资格': '基金从业资格',
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'保险从业资格CICE': '保险从业资格CICE',
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'经济师': '经济师',
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'税务师': '税务师',
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'期货从业资格': '期货从业资格',
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'理财规划师': '理财规划师',
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'精算师-金融数学': '精算师-金融数学',
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}
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financeIQ_all_sets = list(financeIQ_subject_mapping.keys())
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financeIQ_datasets = []
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for _name in financeIQ_all_sets:
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_ch_name = financeIQ_subject_mapping[_name]
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financeIQ_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template={
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answer: dict(
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begin="</E>",
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round=[
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dict(
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role="HUMAN",
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prompt=f"以下是关于{_ch_name}的单项选择题，请直接给出正确答案的选项。\n题目：{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}"
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),
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dict(role="BOT", prompt=f'答案是: {answer}'),
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])
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for answer in ["A", "B", "C", "D"]
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},
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ice_token="</E>",
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),
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retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
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inferencer=dict(type=PPLInferencer),
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)
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financeIQ_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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financeIQ_datasets.append(
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dict(
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type=FinanceIQDataset,
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path="./data/FinanceIQ/",
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name=_name,
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abbr=f"FinanceIQ-{_name}",
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reader_cfg=dict(
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input_columns=["question", "A", "B", "C", "D"],
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output_column="answer",
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train_split="dev",
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test_split='test'),
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infer_cfg=financeIQ_infer_cfg,
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eval_cfg=financeIQ_eval_cfg,
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))
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del _name, _ch_name
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