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46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
# Most of the code in this file is copied from https://github.com/openai/simple-evals/blob/main/math_eval.py
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from mmengine.config import read_base
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from opencompass.partitioners import NaivePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.summarizers import DefaultSubjectiveSummarizer
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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from opencompass.configs.datasets.SimpleQA.simpleqa_gen import \
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simpleqa_datasets
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from opencompass.configs.models.openai.gpt_4o_2024_05_13 import \
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models as gpt_4o_2024_05_13_model
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models = gpt_4o_2024_05_13_model # model for generation
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judge_models = gpt_4o_2024_05_13_model # model for evaluation
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datasets = sum([v for k, v in locals().items() if k.endswith('_datasets')], [])
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summarizer = dict(type=DefaultSubjectiveSummarizer)
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# -------------Inferen Stage ----------------------------------------
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from opencompass.partitioners import NaivePartitioner, NumWorkerPartitioner
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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infer = dict(
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partitioner=dict(type=NumWorkerPartitioner, num_worker=8),
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runner=dict(type=LocalRunner,
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max_num_workers=8,
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task=dict(type=OpenICLInferTask)),
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)
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eval = dict(
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partitioner=dict(
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type=SubjectiveNaivePartitioner,
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models=[gpt_4o_2024_05_13_model],
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judge_models=[gpt_4o_2024_05_13_model],
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),
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runner=dict(type=LocalRunner,
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max_num_workers=256,
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task=dict(type=SubjectiveEvalTask)),
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)
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