diff --git a/README.md b/README.md
index f6eef38..32f4559 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ Use the pre-trained model to generate a text embedding for the sentence "Hello,
 import towhee
 
 towhee.dc(["Hello, world."]) \
-      .text_embedding.data2vec_text() \
+      .text_embedding.data2vec() \
       .show()
 
 ```
@@ -38,7 +38,7 @@ towhee.dc(["Hello, world."]) \
 
 Create the operator via the following factory method
 
-***data2vec_text()***
+***data2vec()***
 
 <br />
 
diff --git a/__init__.py b/__init__.py
index 4e4f668..c79ee5d 100644
--- a/__init__.py
+++ b/__init__.py
@@ -15,5 +15,5 @@
 from .data2vec_text import Data2VecText
  
  
-def data2vec_text(model_name='facebook/data2vec-vision-base'):
-    return Data2Text(model_name)
+def data2vec(model_name='facebook/data2vec-text-base'):
+    return Data2VecText(model_name)
diff --git a/data2vec_text.py b/data2vec_text.py
index 360cef1..8a14c51 100644
--- a/data2vec_text.py
+++ b/data2vec_text.py
@@ -17,8 +17,8 @@ from transformers import RobertaTokenizer, Data2VecTextModel
 from towhee.operator.base import NNOperator
 
 class Data2VecText(NNOperator):
-    def __init__(self):
-        self.model = Data2VecTextModel.from_pretrained("facebook/data2vec-text-base")
+    def __init__(self, model_name):
+        self.model = model_name
         self.tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
 
     def __call__(self, text: str) -> numpy.ndarray:
diff --git a/requirements.txt b/requirements.txt
index 0805d19..7a53fc5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 numpy
 transformers>4.19.0
 torch
-transformers
 towhee