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modelscope/tests/pipelines/test_sentiment_classification.py

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# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.models import Model
from modelscope.models.nlp import SbertForSentimentClassification
from modelscope.pipelines import SentimentClassificationPipeline, pipeline
from modelscope.preprocessors import SentimentClassificationPreprocessor
from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
class SentimentClassificationTest(unittest.TestCase):
model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
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'
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_direct_file_download(self):
cache_path = snapshot_download(self.model_id)
tokenizer = SentimentClassificationPreprocessor(cache_path)
model = SbertForSentimentClassification(
cache_path, tokenizer=tokenizer)
pipeline1 = SentimentClassificationPipeline(
model, preprocessor=tokenizer)
pipeline2 = pipeline(
Tasks.sentiment_classification,
model=model,
preprocessor=tokenizer)
print(f'sentence1: {self.sentence1}\n'
f'pipeline1:{pipeline1(input=self.sentence1)}')
print()
print(f'sentence1: {self.sentence1}\n'
f'pipeline1: {pipeline2(input=self.sentence1)}')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_model_from_modelhub(self):
model = Model.from_pretrained(self.model_id)
tokenizer = SentimentClassificationPreprocessor(model.model_dir)
pipeline_ins = pipeline(
task=Tasks.sentiment_classification,
model=model,
preprocessor=tokenizer)
print(pipeline_ins(input=self.sentence1))
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_model_name(self):
pipeline_ins = pipeline(
task=Tasks.sentiment_classification, model=self.model_id)
print(pipeline_ins(input=self.sentence1))
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_default_model(self):
pipeline_ins = pipeline(task=Tasks.sentiment_classification)
print(pipeline_ins(input=self.sentence1))
if __name__ == '__main__':
unittest.main()