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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>
53 lines
1.8 KiB
Python
Executable File
53 lines
1.8 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 PMMEvalMMMLUDataset, PMMEvalMMMLUEvaluator, pmmeval_mmmlu_postprocess
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NATURAL_LANGUAGE_CODES_MMMLU = ['EN-US', 'ZH-CN', 'AR-XY', 'ES-LA', 'FR-FR', 'JA-JP', 'KO-KR', 'PT-BR', 'TH-TL', 'VI-VT']
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PMMEVAL_MMMLU_TEMPLATE = "The following is a multiple-choice question. Please 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.\n\n{Question}\nA. {A}\nB. {B}\nC. {C}\nD. {D}"
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PMMEval_MMMLU_datasets = []
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PMMEval_MMMLU_reader_cfg = dict(
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input_columns=['Question', 'A', 'B', 'C', 'D'],
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output_column='Answer',
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train_split='test')
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PMMEval_MMMLU_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_MMMLU_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_MMMLU:
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PMMEval_MMMLU_eval_cfg = dict(
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evaluator=dict(type=PMMEvalMMMLUEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=pmmeval_mmmlu_postprocess, lang_code=lang_code))
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PMMEval_MMMLU_datasets.append(
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dict(
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abbr=f'mmmlu-{lang_code}',
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type=PMMEvalMMMLUDataset,
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path='P-MMEval',
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lang=lang_code,
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difficulty='all',
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reader_cfg=PMMEval_MMMLU_reader_cfg,
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infer_cfg=PMMEval_MMMLU_infer_cfg,
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eval_cfg=PMMEval_MMMLU_eval_cfg)
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)
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