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61 lines
1.8 KiB
Python
61 lines
1.8 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 PMMEvalXNLIDataset, PMMEvalXNLIEvaluator, pmmeval_xnli_postprocess
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NATURAL_LANGUAGE_CODES = ['en', 'zh', 'ar', 'es', 'fr', 'ja', 'ko', 'pt', 'th', 'vi']
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PMMEVAL_XNLI_TEMPLATE = """Take the following as truth: {premise}
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Then the following statement: \"{statement}\" is
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Options:
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A. true
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B. inconclusive
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C. false
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Select the correct option from A, B, and C, and return it in the following JSON format:
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{"answer": "[choice]"}
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where [choice] must be one of A, B, and C."""
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PMMEval_XNLI_datasets = list()
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# Add flores_200
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PMMEval_XNLI_reader_cfg = dict(
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input_columns=['premise', 'statement'],
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output_column='answer',
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test_split='test'
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)
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PMMEval_XNLI_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_XNLI_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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for lang_code in NATURAL_LANGUAGE_CODES:
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PMMEval_XNLI_eval_cfg = dict(
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evaluator=dict(type=PMMEvalXNLIEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=pmmeval_xnli_postprocess, lang_code=lang_code))
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PMMEval_XNLI_datasets.append(
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dict(
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abbr=f'xnli-{lang_code}',
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type=PMMEvalXNLIDataset,
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path='P-MMEval',
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lang=lang_code,
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reader_cfg=PMMEval_XNLI_reader_cfg,
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infer_cfg=PMMEval_XNLI_infer_cfg,
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eval_cfg=PMMEval_XNLI_eval_cfg)
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)
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