update pipeline registry info

modelscope/models/nlp/nli_model.py

@@ -16,7 +16,7 @@ from ..builder import MODELS
 __all__ = ['SbertForNLI']
 
 
-class TextClassifier(SbertPreTrainedModel):
+class SbertTextClassifier(SbertPreTrainedModel):
 
     def __init__(self, config):
         super().__init__(config)
@@ -53,7 +53,8 @@ class SbertForNLI(Model):
         super().__init__(model_dir, *args, **kwargs)
         self.model_dir = model_dir
 
-        self.model = TextClassifier.from_pretrained(model_dir, num_labels=3)
+        self.model = SbertTextClassifier.from_pretrained(
+            model_dir, num_labels=3)
         self.model.eval()
 
     def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:

modelscope/pipelines/builder.py

@@ -18,7 +18,8 @@ PIPELINES = Registry('pipelines')
 DEFAULT_MODEL_FOR_PIPELINE = {
     # TaskName: (pipeline_module_name, model_repo)
     Tasks.image_matting: ('image-matting', 'damo/image-matting-person'),
-    Tasks.nli: ('nli', 'damo/nlp_structbert_nli_chinese-base'),
+    Tasks.nli: ('nlp_structbert_nli_chinese-base',
+                'damo/nlp_structbert_nli_chinese-base'),
     Tasks.text_classification:
     ('bert-sentiment-analysis', 'damo/bert-base-sst2'),
     Tasks.text_generation: ('palm', 'damo/nlp_palm_text-generation_chinese'),

tests/pipelines/test_nli.py

@@ -15,6 +15,7 @@ class NLITest(unittest.TestCase):
     sentence1 = '四川商务职业学院和四川财经职业学院哪个好？'
     sentence2 = '四川商务职业学院商务管理在哪个校区？'
 
+    @unittest.skip('skip temporarily to save test time')
     def test_run_from_local(self):
         cache_path = snapshot_download(self.model_id)
         tokenizer = NLIPreprocessor(cache_path)