diff --git a/README.md b/README.md
index 18cea47..442e6f4 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 <br />
 
-## Desription
+## Description
 
 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.
@@ -21,7 +21,7 @@ The original model was proposed in REALM: Retrieval-Augmented Language Model Pre
 
 ## Code Example
 
-Use the pretrained model "google/realm-cc-news-pretrained-embedder"
+Use the pre-trained model "google/realm-cc-news-pretrained-embedder"
 to generate a text embedding for the sentence "Hello, world.". 
 
 *Write the pipeline*:
@@ -37,7 +37,7 @@ towhee.dc(["Hello, world."]) \
 
 ## Factory Constructor
 
-Create the operator via the following factory method
+Create the operator via the following factory method:
 
 ***text_embedding.transformers(model_name="google/realm-cc-news-pretrained-embedder")***
 
@@ -48,15 +48,15 @@ Create the operator via the following factory method
 The model name in string.
 The default value is "google/realm-cc-news-pretrained-embedder".
 
-Supported model names:
+Supported model name:
 - google/realm-cc-news-pretrained-embedder
 
 <br />
 
 ## Interface
 
-The operator takes a text in string as input.
-It loads tokenizer and pre-trained model using model name.
+The operator takes a piece of text in string as input.
+It loads tokenizer and pre-trained model using model name
 and then return text embedding in ndarray.