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60 lines
2.1 KiB
Python
60 lines
2.1 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.datasets.NPHardEval import (
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hard_GCP_Dataset, hard_GCP_Evaluator,
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hard_TSP_Dataset, hard_TSP_Evaluator,
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hard_MSP_Dataset, hard_MSP_Evaluator,
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cmp_GCP_D_Dataset, cmp_GCP_D_Evaluator,
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cmp_TSP_D_Dataset, cmp_TSP_D_Evaluator,
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cmp_KSP_Dataset, cmp_KSP_Evaluator,
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p_BSP_Dataset, p_BSP_Evaluator,
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p_EDP_Dataset, p_EDP_Evaluator,
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p_SPP_Dataset, p_SPP_Evaluator,
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)
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NPHardEval_tasks = [
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["hard_GCP", "GCP", hard_GCP_Dataset, hard_GCP_Evaluator],
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["hard_TSP", "TSP", hard_TSP_Dataset, hard_TSP_Evaluator],
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["hard_MSP", "MSP", hard_MSP_Dataset, hard_MSP_Evaluator],
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["cmp_GCP_D", "GCP_Decision", cmp_GCP_D_Dataset, cmp_GCP_D_Evaluator],
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["cmp_TSP_D", "TSP_Decision", cmp_TSP_D_Dataset, cmp_TSP_D_Evaluator],
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["cmp_KSP", "KSP", cmp_KSP_Dataset, cmp_KSP_Evaluator],
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["p_BSP", "BSP", p_BSP_Dataset, p_BSP_Evaluator],
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["p_EDP", "EDP", p_EDP_Dataset, p_EDP_Evaluator],
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["p_SPP", "SPP", p_SPP_Dataset, p_SPP_Evaluator],
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]
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NPHardEval_datasets = []
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for name, path_name, dataset, evaluator in NPHardEval_tasks:
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NPHardEval_reader_cfg = dict(input_columns=["prompt", "level"], output_column="q")
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NPHardEval_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template=dict(
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begin="</E>",
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round=[
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dict(role="HUMAN", prompt="</E>{prompt}"),
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dict(role="BOT", prompt=""),
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],
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),
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ice_token="</E>",
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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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NPHardEval_eval_cfg = dict(evaluator=dict(type=evaluator), pred_role="BOT")
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NPHardEval_datasets.append(
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dict(
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type=dataset,
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abbr=name,
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path=f"./data/NPHardEval/{path_name}/",
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reader_cfg=NPHardEval_reader_cfg,
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infer_cfg=NPHardEval_infer_cfg,
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eval_cfg=NPHardEval_eval_cfg,
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)
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)
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