diff --git a/README.md b/README.md
index b68f721..d2c459c 100644
--- a/README.md
+++ b/README.md
@@ -4,17 +4,17 @@
 
 <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.
-This operator is implemented with pretrained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
+This operator is implemented with pre-trained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
 
 <br />
 
 ## Code Example
 
-Use the pretrained model 'distilbert-base-cased'
+Use the pre-trained model 'distilbert-base-cased'
 to generate a text embedding for the sentence "Hello, world.".
 
 *Write the pipeline*:
@@ -30,7 +30,7 @@ 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="bert-base-uncased")***
 
@@ -38,10 +38,10 @@ Create the operator via the following factory method
 
 ***model_name***: *str*
 
-​	The model name in string.
+The model name in string.
 The default model name is "bert-base-uncased".
 
-​	Supported model names:
+Supported model names:
 
 <details><summary>Albert</summary>
 
@@ -294,7 +294,7 @@ The default model name is "bert-base-uncased".
 
 ## Interface
 
-The operator takes a text in string as input.
+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.