diff --git a/README.md b/README.md
index 1090705..2c4731f 100644
--- a/README.md
+++ b/README.md
@@ -56,18 +56,13 @@ result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
 
 **Face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s)
 
-[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action.
+[Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
+
 
 ```python
 df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
 ```
 
-It's going to look for the identity of input image in the database path and it will return pandas data frame as output.
-
-```python
-assert isinstance(df, pd.DataFrame)
-```
-
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p>
 
 **Embeddings**
@@ -82,7 +77,7 @@ This function returns an array as output. The size of the output array would be
 
 ```python
 assert isinstance(embedding, list)
-assert model_name = "VGG-Face" and len(embedding) = 2622
+assert (model_name = "VGG-Face" and len(embedding) == 2622) or (model_name = "Facenet" and len(embedding) == 128)
 ```
 
 Here, embedding is also plotted with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right.