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67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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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.task_models.text_classification import \
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ModelForTextClassification
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import TextClassificationPipeline
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from modelscope.preprocessors import TextClassificationTransformersPreprocessor
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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 SentimentClassificationTaskModelTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.text_classification
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self.model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
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sentence1 = '启动的时候很大声音，然后就会听到1.2秒的卡察的声音，类似齿轮摩擦的声音'
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_run_with_direct_file_download(self):
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cache_path = snapshot_download(self.model_id)
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tokenizer = TextClassificationTransformersPreprocessor(cache_path)
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model = ModelForTextClassification.from_pretrained(
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self.model_id, num_labels=2)
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pipeline1 = TextClassificationPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.text_classification, model=model, preprocessor=tokenizer)
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print(f'sentence1: {self.sentence1}\n'
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f'pipeline1:{pipeline1(input=self.sentence1)}')
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print(f'sentence1: {self.sentence1}\n'
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f'pipeline1: {pipeline2(input=self.sentence1)}')
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@unittest.skipUnless(test_level() >= 2, '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 = TextClassificationTransformersPreprocessor(model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.text_classification,
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model=model,
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preprocessor=tokenizer)
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print(pipeline_ins(input=self.sentence1))
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self.assertTrue(
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isinstance(pipeline_ins.model, ModelForTextClassification))
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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(
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task=Tasks.text_classification, model=self.model_id)
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print(pipeline_ins(input=self.sentence1))
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self.assertTrue(
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isinstance(pipeline_ins.model, ModelForTextClassification))
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@unittest.skipUnless(test_level() >= 0, '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.text_classification)
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print(pipeline_ins(input=self.sentence1))
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self.assertTrue(
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isinstance(pipeline_ins.model, ModelForTextClassification))
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if __name__ == '__main__':
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unittest.main()
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