mirror of
https://github.com/modelscope/modelscope.git
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83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
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# 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 SentenceEmbedding
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import SentenceEmbeddingPipeline
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from modelscope.preprocessors import SentenceEmbeddingPreprocessor
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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 SentenceEmbeddingTest(unittest.TestCase):
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model_id = 'damo/nlp_corom_sentence-embedding_english-base'
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inputs = {
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'source_sentence': ["how long it take to get a master's degree"],
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'sentences_to_compare': [
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"On average, students take about 18 to 24 months to complete a master's degree.",
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'On the other hand, some students prefer to go at a slower pace and choose to take ',
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'several years to complete their studies.',
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'It can take anywhere from two semesters'
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]
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}
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inputs2 = {
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'source_sentence': ["how long it take to get a master's degree"],
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'sentences_to_compare': [
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"On average, students take about 18 to 24 months to complete a master's degree."
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]
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}
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inputs3 = {
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'source_sentence': ["how long it take to get a master's degree"],
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'sentences_to_compare': []
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}
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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 = SentenceEmbeddingPreprocessor(cache_path)
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model = SentenceEmbedding.from_pretrained(cache_path)
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pipeline1 = SentenceEmbeddingPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.sentence_embedding, model=model, preprocessor=tokenizer)
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print(f'inputs: {self.inputs}\n'
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f'pipeline1:{pipeline1(input=self.inputs)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.inputs)}')
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print()
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print(f'inputs: {self.inputs2}\n'
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f'pipeline1:{pipeline1(input=self.inputs2)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.inputs2)}')
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print(f'inputs: {self.inputs3}\n'
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f'pipeline1:{pipeline1(input=self.inputs3)}')
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print()
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print(f'pipeline2: {pipeline2(input=self.inputs3)}')
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@unittest.skipUnless(test_level() >= 0, '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 = SentenceEmbeddingPreprocessor(model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.sentence_embedding, model=model, preprocessor=tokenizer)
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print(pipeline_ins(input=self.inputs))
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@unittest.skipUnless(test_level() >= 1, '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.sentence_embedding, model=self.model_id)
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print(pipeline_ins(input=self.inputs))
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@unittest.skipUnless(test_level() >= 2, '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.sentence_embedding)
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print(pipeline_ins(input=self.inputs))
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if __name__ == '__main__':
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unittest.main()
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