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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
107 lines
4.0 KiB
Python
107 lines
4.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import numpy as np
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from modelscope.hub.api import HubApi
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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 SbertForFaqQuestionAnswering
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import FaqQuestionAnsweringPipeline
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from modelscope.preprocessors import \
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FaqQuestionAnsweringTransformersPreprocessor
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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.test_utils import test_level
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class FaqQuestionAnsweringTest(unittest.TestCase, DemoCompatibilityCheck):
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def setUp(self) -> None:
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self.task = Tasks.faq_question_answering
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self.model_id = 'damo/nlp_structbert_faq-question-answering_chinese-base'
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self.model_id_multilingual = 'damo/nlp_faq-question-answering_multilingual-base'
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param = {
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'query_set': ['如何使用优惠券', '在哪里领券', '在哪里领券'],
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'support_set': [{
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'text': '卖品代金券怎么用',
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'label': '6527856'
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}, {
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'text': '怎么使用优惠券',
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'label': '6527856'
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}, {
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'text': '这个可以一起领吗',
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'label': '1000012000'
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}, {
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'text': '付款时送的优惠券哪里领',
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'label': '1000012000'
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}, {
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'text': '购物等级怎么长',
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'label': '13421097'
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}, {
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'text': '购物等级二心',
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'label': '13421097'
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}]
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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_with_direct_file_download(self):
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cache_path = snapshot_download(self.model_id)
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preprocessor = FaqQuestionAnsweringTransformersPreprocessor.from_pretrained(
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cache_path)
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model = SbertForFaqQuestionAnswering.from_pretrained(cache_path)
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pipeline_ins = FaqQuestionAnsweringPipeline(
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model, preprocessor=preprocessor)
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result = pipeline_ins(self.param)
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print(result)
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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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preprocessor = FaqQuestionAnsweringTransformersPreprocessor(
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model.model_dir)
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pipeline_ins = pipeline(
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task=Tasks.faq_question_answering,
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model=model,
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preprocessor=preprocessor)
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result = pipeline_ins(self.param)
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print(result)
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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.faq_question_answering, model=self.model_id)
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result = pipeline_ins(self.param)
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print(result)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_multilingual_model(self):
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pipeline_ins = pipeline(
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task=Tasks.faq_question_answering,
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model=self.model_id_multilingual,
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model_revision='v1.0.0')
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result = pipeline_ins(self.param)
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print(result)
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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.faq_question_answering)
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print(pipeline_ins(self.param, max_seq_length=20))
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_sentence_embedding(self):
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pipeline_ins = pipeline(task=Tasks.faq_question_answering)
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sentence_vec = pipeline_ins.get_sentence_embedding(
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['今天星期六', '明天星期几明天星期几'])
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print(np.shape(sentence_vec))
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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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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