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新增模型测试用例 1. 多轮对话改写模型：damo/nlp_mt5_dialogue-rewriting_chinese-base 2. 多语言FAQ模型：damo/nlp_faq-question-answering_multilingual-base 3. fact-checking模型：damo/nlp_structbert_fact-checking_chinese-base 4. 电商领域文本相似度模型：damo/nlp_structbert_sentence-similarity_chinese-retail-base 均不涉及sdk更新，仅新增模型 Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11477190
76 lines
3.4 KiB
Python
76 lines
3.4 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.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.demo_utils import DemoCompatibilityCheck
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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 NLITest(unittest.TestCase, DemoCompatibilityCheck):
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def setUp(self) -> None:
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self.task = Tasks.nli
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self.model_id = 'damo/nlp_structbert_nli_chinese-base'
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self.model_id_fact_checking = 'damo/nlp_structbert_fact-checking_chinese-base'
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sentence1 = '四川商务职业学院和四川财经职业学院哪个好？'
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sentence2 = '四川商务职业学院商务管理在哪个校区？'
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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 = TextClassificationTransformersPreprocessor(cache_path)
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model = Model.from_pretrained(cache_path)
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pipeline1 = TextClassificationPipeline(model, preprocessor=tokenizer)
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pipeline2 = pipeline(Tasks.nli, model=model, preprocessor=tokenizer)
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print(f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
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f'pipeline1:{pipeline1(input=(self.sentence1, self.sentence2))}')
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print(
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f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
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f'pipeline1: {pipeline2(input=(self.sentence1, self.sentence2))}')
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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.nli, model=model, preprocessor=tokenizer)
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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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.nli, 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_nli',
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compare_fn=IgnoreKeyFn('.*intermediate_act_fn')):
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_fact_checking_model(self):
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pipeline_ins = pipeline(
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task=Tasks.nli,
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model=self.model_id_fact_checking,
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model_revision='v1.0.1')
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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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.nli)
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print(pipeline_ins(input=(self.sentence1, self.sentence2)))
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@unittest.skip('demo compatibility test is only enabled on a needed-basis')
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def test_demo_compatibility(self):
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self.compatibility_check()
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if __name__ == '__main__':
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unittest.main()
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