diff --git a/README.md b/README.md
index 34895c4..b87ca52 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
-# Text Embedding with Dpr
+# Text Embedding with DPR
 
 *author: Kyle He*
 
 <br />
 
-## Desription
+## Description
 
 This operator uses Dense Passage Retrieval (DPR) to convert long text to embeddings.
 
@@ -14,7 +14,7 @@ Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, We
 
 **DPR** models were proposed in "Dense Passage Retrieval for Open-Domain Question Answering"[2].
 
-In this work, we show that retrieval can be practically implemented using dense representations alone,
+In this work, they show that retrieval can be practically implemented using dense representations alone,
 where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework[2].
 
 ### References
@@ -27,7 +27,7 @@ where embeddings are learned from a small number of questions and passages by a
 
 ## Code Example
 
-Use the pretrained model "facebook/dpr-ctx_encoder-single-nq-base"
+Use the pre-trained model "facebook/dpr-ctx_encoder-single-nq-base"
 to generate a text embedding for the sentence "Hello, world.". 
 
 *Write the pipeline*:
@@ -43,7 +43,7 @@ towhee.dc(["Hello, world."]) \
 
 ## Factory Constructor
 
-Create the operator via the following factory method
+Create the operator via the following factory method:
 
 ***text_embedding.dpr(model_name="facebook/dpr-ctx_encoder-single-nq-base")***
 
@@ -63,7 +63,7 @@ Supported model names:
 ## Interface
 
 The operator takes a text in string as input.
-It loads tokenizer and pre-trained model using model name.
+It loads tokenizer and pre-trained model using model name
 and then return text embedding in ndarray.
 
 **Parameters:**