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90efcf2216
* Update with PMMEval * Update * Update __init__.py * Fix Bugs * Delete .pre-commit-config.yaml * Pull merge --------- Co-authored-by: liushz <qq1791167085@163.com>
51 lines
1.9 KiB
Python
Executable File
51 lines
1.9 KiB
Python
Executable File
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 PMMEvalMLogiQADataset, PMMEvalMLogiQAEvaluator, pmmeval_mlogiqa_postprocess
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NATURAL_LANGUAGE_CODES = ['en', 'zh', 'ar', 'es', 'fr', 'ja', 'ko', 'pt', 'th', 'vi']
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PMMEVAL_MLOGIQA_TEMPLATE = "Passage: {context}\nQuestion: {question}\nChoices:\nA.{option_1}\nB.{option_2}\nC.{option_3}\nD.{option_4}\nPlease choose the most suitable one among A, B, C and D as the answer to this question, and return it in the following JSON format:\n{'answer': '[choice]'}\nwhere [choice] must be one of A, B, C and D."
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PMMEval_MLogiQA_datasets = []
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PMMEval_MLogiQA_reader_cfg = dict(
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input_columns=['context', 'question', 'option_1', 'option_2', 'option_3', 'option_4'],
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output_column='answer',
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train_split='test')
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PMMEval_MLogiQA_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_MLOGIQA_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_MLogiQA_eval_cfg = dict(
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evaluator=dict(type=PMMEvalMLogiQAEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=pmmeval_mlogiqa_postprocess, lang_code=lang_code))
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PMMEval_MLogiQA_datasets.append(
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dict(
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abbr=f'mlogiqa-{lang_code}',
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type=PMMEvalMLogiQADataset,
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path='P-MMEval',
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lang=lang_code,
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reader_cfg=PMMEval_MLogiQA_reader_cfg,
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infer_cfg=PMMEval_MLogiQA_infer_cfg,
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eval_cfg=PMMEval_MLogiQA_eval_cfg)
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)
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