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https://github.com/modelscope/modelscope.git
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84 lines
3.6 KiB
Python
84 lines
3.6 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 import SbertForSequenceClassification
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import ZeroShotClassificationPipeline
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from modelscope.preprocessors import \
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ZeroShotClassificationTransformersPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.regress_test_utils import IgnoreKeyFn, MsRegressTool
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from modelscope.utils.test_utils import test_level
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class ZeroShotClassificationTest(unittest.TestCase):
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def setUp(self) -> None:
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self.task = Tasks.zero_shot_classification
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self.model_id = 'damo/nlp_structbert_zero-shot-classification_chinese-base'
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sentence = '全新突破 解放军运20版空中加油机曝光'
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labels = ['文化', '体育', '娱乐', '财经', '家居', '汽车', '教育', '科技', '军事']
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labels_str = '文化, 体育, 娱乐, 财经, 家居, 汽车, 教育, 科技, 军事'
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template = '这篇文章的标题是{}'
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regress_tool = MsRegressTool(baseline=False)
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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 = ZeroShotClassificationTransformersPreprocessor(cache_path)
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model = SbertForSequenceClassification.from_pretrained(cache_path)
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pipeline1 = ZeroShotClassificationPipeline(
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model, preprocessor=tokenizer)
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pipeline2 = pipeline(
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Tasks.zero_shot_classification,
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model=model,
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preprocessor=tokenizer)
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print(
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f'sentence: {self.sentence}\n'
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f'pipeline1:{pipeline1(input=self.sentence,candidate_labels=self.labels)}'
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)
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print(
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f'sentence: {self.sentence}\n'
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f'pipeline2: {pipeline2(self.sentence,candidate_labels=self.labels_str,hypothesis_template=self.template)}'
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)
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print(
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f'sentence: {self.sentence}\n'
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f'pipeline2: {pipeline2(self.sentence,candidate_labels=self.labels,hypothesis_template=self.template)}'
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)
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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 = ZeroShotClassificationTransformersPreprocessor(
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model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.zero_shot_classification,
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model=model,
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preprocessor=tokenizer)
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print(pipeline_ins(input=self.sentence, candidate_labels=self.labels))
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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.zero_shot_classification, model=self.model_id)
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with self.regress_tool.monitor_module_single_forward(
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pipeline_ins.model,
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'sbert_zero_shot',
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compare_fn=IgnoreKeyFn('.*intermediate_act_fn')):
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print(
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pipeline_ins(
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input=self.sentence, candidate_labels=self.labels))
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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.zero_shot_classification)
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print(pipeline_ins(input=self.sentence, candidate_labels=self.labels))
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if __name__ == '__main__':
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unittest.main()
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