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edab1c07ba
* add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * udpate dataset for modelscope support * update readme * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * update readme * remove tydiqa japanese subset * add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * update readme * udpate dataset for modelscope support * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * remove tydiqa japanese subset * update util * remove .DS_Store * fix md format * move util into package * update docs/get_started.md * restore eval_api_zhipu_v2.py, add environment setting * Update dataset * Update * Update * Update * Update --------- Co-authored-by: Yun lin <yunlin@U-Q9X2K4QV-1904.local> Co-authored-by: Yunnglin <mao.looper@qq.com> Co-authored-by: Yun lin <yunlin@laptop.local> Co-authored-by: Yunnglin <maoyl@smail.nju.edu.cn> Co-authored-by: zhangsongyang <zhangsongyang@pjlab.org.cn>
60 lines
2.1 KiB
Python
60 lines
2.1 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.datasets.NPHardEval import (
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HardGCPDataset, HardGCPEvaluator,
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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', HardGCPDataset, HardGCPEvaluator],
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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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