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55 lines
1.9 KiB
Python
55 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.datasets.PMMEval import PMMEvalMHellaswagDataset, PMMEvalMHellaswagEvaluator, pmmeval_mhellaswag_postprocess
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NATURAL_LANGUAGE_CODES = ['en', 'zh', 'ar', 'es', 'fr', 'ja', 'ko', 'pt', 'th', 'vi']
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PMMEVAL_MHELLASWAG_TEMPLATE = "Input: {ctx}\nOptions: \nA. {option_1}\nB. {option_2}\nC. {option_3}\nD. {option_4}\nPick the correct ending for the sentence from A, B, C, and D, and return it in the following JSON format:\n{\"answer\": \"[choice]\"}\nwhere [choice] must be one of A, B, C or D."
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PMMEval_MHellaswag_datasets = list()
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PMMEval_MHellaswag_reader_cfg = dict(
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input_columns=['ctx', 'option_1', 'option_2', 'option_3', 'option_4'],
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output_column='label',
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test_split='test'
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)
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PMMEval_MHellaswag_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(
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role='HUMAN',
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prompt=PMMEVAL_MHELLASWAG_TEMPLATE
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)
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]
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)
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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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PMMEval_MHellaswag_datasets = list()
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for lang_code in NATURAL_LANGUAGE_CODES:
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PMMEval_MHellaswag_eval_cfg = dict(
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evaluator=dict(type=PMMEvalMHellaswagEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=pmmeval_mhellaswag_postprocess, lang_code=lang_code)
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)
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PMMEval_MHellaswag_datasets.append(
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dict(
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abbr=f'mhellaswag-{lang_code}',
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type=PMMEvalMHellaswagDataset,
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path='P-MMEval',
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lang=lang_code,
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reader_cfg=PMMEval_MHellaswag_reader_cfg,
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infer_cfg=PMMEval_MHellaswag_infer_cfg,
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eval_cfg=PMMEval_MHellaswag_eval_cfg)
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)
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