mirror of
https://github.com/modelscope/modelscope.git
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85 lines
3.9 KiB
Python
85 lines
3.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import shutil
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models import Model
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from modelscope.models.nlp import (LSTMCRFForPartOfSpeech,
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TokenClassificationModel)
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import TokenClassificationPipeline
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from modelscope.preprocessors import \
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TokenClassificationTransformersPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.test_utils import test_level
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class PartOfSpeechTest(unittest.TestCase):
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model_id = 'damo/nlp_structbert_part-of-speech_chinese-lite'
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lstmcrf_news_model_id = 'damo/nlp_lstmcrf_part-of-speech_chinese-news'
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sentence = '今天天气不错，适合出去游玩'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_by_direct_model_download(self):
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cache_path = snapshot_download(self.model_id)
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tokenizer = TokenClassificationTransformersPreprocessor(cache_path)
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model = TokenClassificationModel.from_pretrained(cache_path)
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pipeline1 = TokenClassificationPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.part_of_speech, model=model, preprocessor=tokenizer)
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print(f'sentence: {self.sentence}\n'
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f'pipeline1:{pipeline1(input=self.sentence)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.sentence)}')
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_lstmcrf_news_by_direct_model_download(self):
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cache_path = snapshot_download(self.lstmcrf_news_model_id)
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tokenizer = TokenClassificationTransformersPreprocessor(cache_path)
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model = LSTMCRFForPartOfSpeech.from_pretrained(cache_path)
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pipeline1 = TokenClassificationPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.part_of_speech, model=model, preprocessor=tokenizer)
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print(f'sentence: {self.sentence}\n'
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f'pipeline1:{pipeline1(input=self.sentence)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.sentence)}')
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_model_from_modelhub(self):
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model = Model.from_pretrained(self.model_id)
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tokenizer = TokenClassificationTransformersPreprocessor(
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model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.part_of_speech, model=model, preprocessor=tokenizer)
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print(pipeline_ins(input=self.sentence))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_lstmcrf_news_with_model_from_modelhub(self):
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model = Model.from_pretrained(self.lstmcrf_news_model_id)
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tokenizer = TokenClassificationTransformersPreprocessor(
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model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.part_of_speech, model=model, preprocessor=tokenizer)
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print(pipeline_ins(input=self.sentence))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_name(self):
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pipeline_ins = pipeline(task=Tasks.part_of_speech, model=self.model_id)
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print(pipeline_ins(input=self.sentence))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_lstmcrf_new_with_model_name(self):
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pipeline_ins = pipeline(
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task=Tasks.part_of_speech, model=self.lstmcrf_news_model_id)
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print(pipeline_ins(input=self.sentence))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_run_with_default_model(self):
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pipeline_ins = pipeline(task=Tasks.part_of_speech)
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print(pipeline_ins(input=self.sentence))
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if __name__ == '__main__':
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unittest.main()
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