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41 lines
1.9 KiB
Python
41 lines
1.9 KiB
Python
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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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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 TheoremQADataset
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TheoremQA_reader_cfg = dict(
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input_columns=['Question', 'Answer_type'],
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output_column='Answer',
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train_split='test')
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TheoremQA_prompt1 = "Please read a math problem, and then think step by step to derive the answer. The answer is decided by Answer Type. " \
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"If the Answer type in [bool], the answer needs to be True or False. " \
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"Else if the Answer type in [integer, float] , The answer needs to be in numerical form. " \
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"Else if the Answer type in [list of integer, list of float] , the answer needs to be a list of number like [2, 3, 4]. " \
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"Else if the Answer type in [option], the answer needs to be an option like (a), (b), (c), (d)." \
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"You need to output the answer in your final sentence like 'Therefore, the answer is ...'."
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TheoremQA_prompt2 = f"Below is an instruction that describes a task, paired with an input that provides further context. " \
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f"Write a response that appropriately completes the request.\n\n### Instruction:\n{TheoremQA_prompt1}\n\n### Input:\n{{Question}}\nAnswer_type:{{Answer_type}}\n### Response:\n"
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TheoremQA_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=TheoremQA_prompt2),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer))
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TheoremQA_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type='TheoremQA'))
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TheoremQA_datasets = [
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dict(
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abbr='TheoremQA',
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type=TheoremQADataset,
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path="./data/TheoremQA/test.csv",
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reader_cfg=TheoremQA_reader_cfg,
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infer_cfg=TheoremQA_infer_cfg,
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eval_cfg=TheoremQA_eval_cfg)
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]
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