diff --git a/README.md b/README.md
index c454afc..3cb0a0b 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ Generate embeddings for the audio "test.wav".
 
 *Write a same pipeline with explicit inputs/outputs name specifications:*
 
-- option 1 (towhee>=0.9.0):
+- **option 1 (towhee>=0.9.0):**
 ```python
 from towhee.dc2 import pipe, ops, DataCollection
 
@@ -36,7 +36,7 @@ DataCollection(p('test.wav')).show()
 ```
 <img src="./result.png" width="800px"/>
 
-- option 2:
+- **option 2:**
 ```python
 import towhee
 
@@ -55,18 +55,17 @@ import towhee
 
 Create the operator via the following factory method
 
-***audio_embedding.nnfp(params=None, model_path=None, framework='pytorch')***
+***audio_embedding.nnfp(model_name='nnfp_default', model_path=None, framework='pytorch')***
 
 **Parameters:**
 
-*params: dict*
+*model_name: str*
 
-A dictionary of model parameters. If None, it will use default parameters to create model.
+Model name to create nnfp model with different parameters.
 
 *model_path: str*
 
 The path to model. If None, it will load default model weights.
-When the path ends with '.onnx', the operator will use onnx inference.
 
 *framework: str*
 
@@ -88,7 +87,6 @@ An audio embedding operator generates vectors in numpy.ndarray given towhee audi
 Input audio data is a list of towhee audio frames.
 The audio input should be at least 1s.
 
-
 **Returns**:
 
 *numpy.ndarray*
@@ -96,6 +94,7 @@ The audio input should be at least 1s.
 Audio embeddings in shape (num_clips, 128).
 Each embedding stands for features of an audio clip with length of 1s.
 
+<br />
 
 ***save_model(format='pytorch', path='default')***