mirror of
https://github.com/modelscope/modelscope.git
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25a2028b54
* add trainer interface * add trainer script * add model init support for pipelineadd pipeline tutorial and fix bugs * add text classification evaluation to maas lib * add quickstart and prepare env doc * relax requirements for torch and sentencepiece * merge release/0.1 and fix conflict * modelhub support for model and pipeline Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8868339
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import unittest
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import zipfile
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from pathlib import Path
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from maas_lib.fileio import File
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from maas_lib.trainers import build_trainer
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from maas_lib.utils.logger import get_logger
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logger = get_logger()
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class SequenceClassificationTrainerTest(unittest.TestCase):
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def test_sequence_classification(self):
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model_url = 'https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com' \
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'/release/easynlp_modelzoo/alibaba-pai/bert-base-sst2.zip'
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cache_path_str = r'.cache/easynlp/bert-base-sst2.zip'
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cache_path = Path(cache_path_str)
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if not cache_path.exists():
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cache_path.parent.mkdir(parents=True, exist_ok=True)
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cache_path.touch(exist_ok=True)
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with cache_path.open('wb') as ofile:
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ofile.write(File.read(model_url))
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with zipfile.ZipFile(cache_path_str, 'r') as zipf:
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zipf.extractall(cache_path.parent)
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path: str = './configs/nlp/sequence_classification_trainer.yaml'
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default_args = dict(cfg_file=path)
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trainer = build_trainer('bert-sentiment-analysis', default_args)
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trainer.train()
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trainer.evaluate()
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if __name__ == '__main__':
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unittest.main()
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...
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