diff --git a/modelscope/models/nlp/heads/token_classification_head.py b/modelscope/models/nlp/heads/token_classification_head.py
index ace3deac..3f19ca67 100644
--- a/modelscope/models/nlp/heads/token_classification_head.py
+++ b/modelscope/models/nlp/heads/token_classification_head.py
@@ -14,6 +14,8 @@ from modelscope.utils.constant import Tasks
 
 @HEADS.register_module(
     Tasks.token_classification, module_name=Heads.token_classification)
+@HEADS.register_module(
+    Tasks.part_of_speech, module_name=Heads.token_classification)
 class TokenClassificationHead(TorchHead):
 
     def __init__(self, **kwargs):
diff --git a/modelscope/models/nlp/task_models/token_classification.py b/modelscope/models/nlp/task_models/token_classification.py
index f3930182..a39f58bf 100644
--- a/modelscope/models/nlp/task_models/token_classification.py
+++ b/modelscope/models/nlp/task_models/token_classification.py
@@ -19,6 +19,8 @@ __all__ = ['TokenClassificationModel']
 
 @MODELS.register_module(
     Tasks.token_classification, module_name=TaskModels.token_classification)
+@MODELS.register_module(
+    Tasks.part_of_speech, module_name=TaskModels.token_classification)
 class TokenClassificationModel(SingleBackboneTaskModelBase):
 
     def __init__(self, model_dir: str, *args, **kwargs):
diff --git a/modelscope/pipelines/builder.py b/modelscope/pipelines/builder.py
index aaea0bb6..8c81118c 100644
--- a/modelscope/pipelines/builder.py
+++ b/modelscope/pipelines/builder.py
@@ -25,6 +25,8 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.word_segmentation:
     (Pipelines.word_segmentation,
      'damo/nlp_structbert_word-segmentation_chinese-base'),
+    Tasks.part_of_speech: (Pipelines.part_of_speech,
+                           'damo/nlp_structbert_part-of-speech_chinese-base'),
     Tasks.token_classification:
     (Pipelines.part_of_speech,
      'damo/nlp_structbert_part-of-speech_chinese-base'),
diff --git a/modelscope/pipelines/nlp/token_classification_pipeline.py b/modelscope/pipelines/nlp/token_classification_pipeline.py
index c57dbf20..055a4b8a 100644
--- a/modelscope/pipelines/nlp/token_classification_pipeline.py
+++ b/modelscope/pipelines/nlp/token_classification_pipeline.py
@@ -18,6 +18,8 @@ __all__ = ['TokenClassificationPipeline']
 
 @PIPELINES.register_module(
     Tasks.token_classification, module_name=Pipelines.part_of_speech)
+@PIPELINES.register_module(
+    Tasks.part_of_speech, module_name=Pipelines.part_of_speech)
 class TokenClassificationPipeline(Pipeline):
 
     def __init__(self,
diff --git a/tests/pipelines/test_part_of_speech.py b/tests/pipelines/test_part_of_speech.py
index 25f4491c..61cdfe73 100644
--- a/tests/pipelines/test_part_of_speech.py
+++ b/tests/pipelines/test_part_of_speech.py
@@ -13,7 +13,7 @@ from modelscope.utils.test_utils import test_level
 
 
 class PartOfSpeechTest(unittest.TestCase):
-    model_id = 'damo/nlp_structbert_part-of-speech_chinese-base'
+    model_id = 'damo/nlp_structbert_part-of-speech_chinese-lite'
     sentence = '今天天气不错，适合出去游玩'
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
@@ -34,20 +34,17 @@ class PartOfSpeechTest(unittest.TestCase):
         model = Model.from_pretrained(self.model_id)
         tokenizer = TokenClassificationPreprocessor(model.model_dir)
         pipeline_ins = pipeline(
-            task=Tasks.token_classification,
-            model=model,
-            preprocessor=tokenizer)
+            task=Tasks.part_of_speech, model=model, preprocessor=tokenizer)
         print(pipeline_ins(input=self.sentence))
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_model_name(self):
-        pipeline_ins = pipeline(
-            task=Tasks.token_classification, model=self.model_id)
+        pipeline_ins = pipeline(task=Tasks.part_of_speech, model=self.model_id)
         print(pipeline_ins(input=self.sentence))
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_default_model(self):
-        pipeline_ins = pipeline(task=Tasks.token_classification)
+        pipeline_ins = pipeline(task=Tasks.part_of_speech)
         print(pipeline_ins(input=self.sentence))