fix typo

modelscope/pipelines/builder.py

@@ -192,11 +192,11 @@ def pipeline(task: str = None,
         try:
             from modelscope.utils.hf_util import sentence_transformers_pipeline
             return sentence_transformers_pipeline(model=model, **kwargs)
-        except Exception as e:
-            logger.error(
+        except Exception:
+            logger.exception(
                 'We could not find a suitable pipeline from modelscope, so we tried to load it using the '
                 'sentence_transformers, but that also failed.')
-            raise e
+            raise
 
     if not pipeline_props and is_transformers_available():
         try:

modelscope/utils/hf_util/pipeline_builder.py

@@ -69,25 +69,24 @@ def sentence_transformers_pipeline(model: str, **kwargs):
             model = snapshot_download(model)
 
     from modelscope.pipelines import Pipeline
+
     class SentenceTransformerPipeline(Pipeline):
         """A wrapper for sentence_transformers.SentenceTransformer to make it compatible
         with the modelscope pipeline conventions."""
 
         def __init__(self, model_path: str, **kwargs):
-            from sentence_transformers import SentenceTransformer
             self.model = SentenceTransformer(model_path, **kwargs)
 
-        def __call__(
-                self,
-                sentences: str | list[str] | None = None,
-                prompt_name: str | None = None,
-                **kwargs
-        ):
+        def __call__(self,
+                     sentences: str | list[str] | None = None,
+                     prompt_name: str | None = None,
+                     **kwargs):
             input_data = kwargs.pop('input', None)
             if input_data is not None:
                 sentences = input_data['source_sentence']
                 res = self.model.encode(sentences, **kwargs)
                 return {'text_embedding': res}
-            return self.model.encode(sentences, prompt_name=prompt_name, **kwargs)
+            return self.model.encode(
+                sentences, prompt_name=prompt_name, **kwargs)
 
     return SentenceTransformerPipeline(model, **kwargs)

tests/utils/test_sentence_embedding_utils.py

@@ -1,20 +1,22 @@
 import unittest
 
-from modelscope.utils.test_utils import test_level
-
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import test_level
+
 
 class SentenceEmbeddingPipelineTest(unittest.TestCase):
+
     def setUp(self) -> None:
         self.model_id = 'Qwen/Qwen3-Embedding-0.6B'
         self.queries = [
-            "What is the capital of China?",
-            "Explain gravity",
+            'What is the capital of China?',
+            'Explain gravity',
         ]
         self.documents = [
-            "The capital of China is Beijing.",
-            "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
+            'The capital of China is Beijing.',
+            'Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and '
+            'is responsible for the movement of planets around the sun.',
         ]
 
     def test_ori_pipeline(self):
@@ -23,8 +25,8 @@ class SentenceEmbeddingPipelineTest(unittest.TestCase):
             model=self.model_id,
             model_revision='master',
         )
-        inputs = {"source_sentence": self.documents}
-        embeddings = ppl(input=inputs)["text_embedding"]
+        inputs = {'source_sentence': self.documents}
+        embeddings = ppl(input=inputs)['text_embedding']
         self.assertEqual(embeddings.shape[0], len(self.documents))
         self.assertLess((embeddings[0][0] + 0.0471825), 0.01)  # check value
 
@@ -34,10 +36,10 @@ class SentenceEmbeddingPipelineTest(unittest.TestCase):
             model=self.model_id,
             model_revision='master',
         )
-        embeddings = ppl(self.queries, prompt_name="query")
+        embeddings = ppl(self.queries, prompt_name='query')
         self.assertEqual(embeddings.shape[0], len(self.documents))
         self.assertLess((embeddings[0][0] + 0.050865322), 0.01)  # check value
 
 
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()