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42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
from mmengine.config import read_base
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with read_base():
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# Import models
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# Import datasets
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from opencompass.configs.datasets.MathBench.mathbench_gen import \
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mathbench_datasets
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from opencompass.configs.models.hf_internlm.hf_internlm2_chat_7b import \
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models as internlm2_chat_7b_model
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from opencompass.configs.models.hf_llama.hf_llama3_8b_instruct import \
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models as llama3_8b_instruct_model
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# Import summarizers for display results
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from opencompass.configs.summarizers.groups.mathbench_v1_2024 import \
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summarizer # Grouped results for MathBench-A and MathBench-T separately
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# from opencompass.configs.summarizers.mathbench_v1 import summarizer # Detailed results for every sub-dataset
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# from opencompass.configs.summarizers.groups.mathbench_v1_2024_lang import summarizer # Grouped results for bilingual results
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datasets = sum([v for k, v in locals().items() if k.endswith('_datasets')], [])
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models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
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from opencompass.partitioners import NaivePartitioner, NumWorkerPartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask
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eval = dict(
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partitioner=dict(type=NaivePartitioner, n=8),
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runner=dict(type=LocalRunner,
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max_num_workers=256,
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task=dict(type=OpenICLEvalTask)),
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)
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infer = dict(
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partitioner=dict(type=NumWorkerPartitioner, num_worker=4),
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runner=dict(type=LocalRunner,
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max_num_workers=256,
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task=dict(type=OpenICLInferTask)),
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)
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work_dir = './outputs/mathbench_results'
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