diff --git a/README.md b/README.md
index 85c9cea..8ff6262 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,8 @@
-# Text Embedding with longformer
+# Text Embedding with dpr
 
 *author: Kyle He*
 
 
-
 ## Desription
 
 This operator uses Longformer to convert long text to embeddings.
@@ -23,26 +22,42 @@ length, making it easy to process documents of thousands of tokens or longer[2].
 
 [2].https://arxiv.org/pdf/2004.05150.pdf
 
+## Code Example
+
+Use the pretrained model "facebook/dpr-ctx_encoder-single-nq-base"
+to generate a text embedding for the sentence "Hello, world.". 
+
+ *Write the pipeline*:
+
 ```python
-from towhee import ops
+from towhee import dc
 
-text_encoder = ops.text_embedding.longformer(model_name="allenai/longformer-base-4096")
-text_embedding = text_encoder("Hello, world.")
+
+dc.stream(["Hello, world."])
+  .text_embedding.longformer("allenai/longformer-base-4096")
+  .show()
 ```
 
 ## Factory Constructor
 
 Create the operator via the following factory method
 
-***ops.text_embedding.longformer(model_name)***
+***text_embedding.dpr(model_name="allenai/longformer-base-4096")***
 
+**Parameters:**
 
+​	***model_name***: *str*
 
-## Interface
+​	The model name in string.
+The default value is "allenai/longformer-base-4096".
+You can get the list of supported model names by calling `get_model_list` from [longformer.py](https://towhee.io/text-embedding/longformer/src/branch/main/longformer.py).
 
-A text embedding operator takes a sentence, paragraph, or document in string as an input
-and output an embedding vector in ndarray which captures the input's core semantic elements.
 
+## Interface
+
+The operator takes a text in string as input.
+It loads tokenizer and pre-trained model using model name.
+and then return text embedding in ndarray.
 
 **Parameters:**
 
@@ -52,34 +67,8 @@ and output an embedding vector in ndarray which captures the input's core semant
 
 
 
-**Returns**: *numpy.ndarray*
+**Returns**:
 
-​	The text embedding extracted by model.
+​    *numpy.ndarray*
 
-
-
-## Code Example
-
-Use the pretrained model ('allenai/longformer-base-4096')
-to generate a text embedding for the sentence "Hello, world.". 
-
- *Write the pipeline in simplified style*:
-
-```python
-import towhee.DataCollection as dc
-
-dc.glob("Hello, world.")
-  .text_embedding.longformer('longformer-base-4096')
-  .show()
-```
-
-*Write a same pipeline with explicit inputs/outputs name specifications:*
-
-```python
-from towhee import DataCollection as dc
-
-dc.glob['text']('Hello, world.')
-  .text_embedding.longformer['text', 'vec']('longformer-base-4096')
-  .select('vec')
-  .show()
-```
+​	The text embedding extracted by model.
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
index 2832034..22be76d 100644
--- a/__init__.py
+++ b/__init__.py
@@ -15,5 +15,5 @@
 from .longformer import Longformer
 
 
-def longformer(model_name: str):
-    return Longformer(model_name)
+def longformer(**kwargs):
+    return Longformer(**kwargs)
diff --git a/longformer.py b/longformer.py
index 3126249..f0f420a 100644
--- a/longformer.py
+++ b/longformer.py
@@ -1,14 +1,16 @@
 import numpy
 import torch
 from transformers import LongformerTokenizer, LongformerModel
-import logging
 
 from towhee.operator import NNOperator
 from towhee import register
 
-
 import warnings
+import logging
+
+
 warnings.filterwarnings('ignore')
+logging.getLogger("transformers").setLevel(logging.ERROR)
 log = logging.getLogger()
 
 
@@ -24,7 +26,7 @@ class Longformer(NNOperator):
         model_name (`str`):
             Which model to use for the embeddings.
     """
-    def __init__(self, model_name: str) -> None:
+    def __init__(self, model_name: str = 'allenai/longformer-base-4096') -> None:
         super().__init__()
         self.model_name = model_name
         try:
@@ -55,5 +57,17 @@ class Longformer(NNOperator):
         except Exception as e:
             log.error(f'Fail to extract features by model: {self.model_name}')
             raise e
-        feature_vector = feature_vector.detach().numpy()
-        return feature_vector
+        vec = feature_vector.detach().numpy()
+        return vec
+
+
+def get_model_list():
+    full_list = [
+        "allenai/longformer-base-4096",
+        "allenai/longformer-large-4096",
+        "allenai/longformer-large-4096-finetuned-triviaqa",
+        "allenai/longformer-base-4096-extra.pos.embd.only",
+        "allenai/longformer-large-4096-extra.pos.embd.only",
+    ]
+    full_list.sort()
+    return full_list