mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-16 16:27:45 +01:00
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9662406 * bug fix for nlp backbone-head trainers
67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
# 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.task_models.sequence_classification import \
|
||
SequenceClassificationModel
|
||
from modelscope.pipelines import pipeline
|
||
from modelscope.pipelines.nlp import SingleSentenceClassificationPipeline
|
||
from modelscope.preprocessors import SingleSentenceClassificationPreprocessor
|
||
from modelscope.utils.constant import Tasks
|
||
from modelscope.utils.test_utils import test_level
|
||
|
||
|
||
class SentimentClassificationTaskModelTest(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 = SingleSentenceClassificationPreprocessor(cache_path)
|
||
model = SequenceClassificationModel.from_pretrained(
|
||
self.model_id, num_labels=2)
|
||
pipeline1 = SingleSentenceClassificationPipeline(
|
||
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() >= 2, 'skip test in current test level')
|
||
def test_run_with_model_from_modelhub(self):
|
||
model = Model.from_pretrained(self.model_id)
|
||
tokenizer = SingleSentenceClassificationPreprocessor(model.model_dir)
|
||
pipeline_ins = pipeline(
|
||
task=Tasks.sentiment_classification,
|
||
model=model,
|
||
preprocessor=tokenizer)
|
||
print(pipeline_ins(input=self.sentence1))
|
||
self.assertTrue(
|
||
isinstance(pipeline_ins.model, SequenceClassificationModel))
|
||
|
||
@unittest.skipUnless(test_level() >= 0, '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))
|
||
self.assertTrue(
|
||
isinstance(pipeline_ins.model, SequenceClassificationModel))
|
||
|
||
@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))
|
||
self.assertTrue(
|
||
isinstance(pipeline_ins.model, SequenceClassificationModel))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
unittest.main()
|