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+++ b/README.en.md
@@ -2,57 +2,56 @@
 
 [查看中文](README.md)
 
-TF_Adapter is committed to providing the outstanding computing power of Ascend AI Processors to developers who use the TensorFlow framework.
-Developers only need to install the TF_Adapter plug-in and add a small amount of configuration to the existing TensorFlow script to accelerate their training jobs on the Ascend AI Processors.
+TensorFlow Adapter For Ascend (TF_Adapter) is committed to providing outstanding computing power of Ascend AI Processors to developers who use the TensorFlow framework.
+Developers only need to install the TF_Adapter plugin and add a small amount of configuration to the existing TensorFlow script to accelerate their training jobs on Ascend AI Processors.
 
 ![tfadapter](https://images.gitee.com/uploads/images/2020/1027/094640_8f305b88_8175427.jpeg "framework.jpg")
 
-You can read [TF_Adapter Interface](https://support.huaweicloud.com/intl/en-us/ug-tf-training-tensorflow/atlasadapi_13_0004.html) for more details。
+You can read [TF_Adapter Interface](https://support.huaweicloud.com/intl/en-us/ug-tf-training-tensorflow/atlasadapi_13_0004.html) for more details.
 ## Installation Guide
-### Building from source
+### Build from Source Code
 
-You can build the TF_Adapter software package from the source code and install it on the Ascend AI Processor environment.
-> The TF_Adapter plug-in has a strict matching relationship with TensorFlow. Before building from source code, you need to ensure that it has been installed correctly [TensorFlow v1.15.0
->Version](https://www.tensorflow.org/install/pip).
+You can build the TF_Adapter software package from the source code and install it on an Ascend AI Processor environment.
+> The TF_Adapter plugin has a strict matching relationship with TensorFlow. Before building from the source code, ensure that [TensorFlow v1.15.0]
+> (https://www.tensorflow.org/install/pip) has been correctly installed.
 
-You may also build GraphEngine from the source. To build GraphEngine, please make sure that you have access to an Ascend 910 environment as compiling environment, and make sure that the following software requirements are fulfilled.
+You may also compile TF_Adapter from the source code. Before compiling from the source code, ensure that you have an Ascend 910 AI processor environment that meets the following requirements:
 - Linux OS
-- GCC >= 7.3.0
-- CMake >= 3.14.0
+- GCC ≥ 7.3.0
+- CMake ≥ 3.14.0
 - [SWIG](http://www.swig.org/download.html)
 
-#### Download
+#### Download the source code.
 ```
 git clone https://gitee.com/ascend/tensorflow.git
 cd tensorflow
 ```
 
-#### Execute the script to generate the installation package
+#### Execute the script to generate the installation package.
 ```
 chmod +x build.sh
 ./build.sh
 ```
 
 
-After the script is successfully executed, a compressed file of tfadapter.tar will be generated in the output directory.
+After the script is successfully executed, a compressed file **tfadapter.tar** will be generated in the **output** directory.
 
-#### Install
-Unzip the tfadapter.tar file to generate npu_bridge-1.15.0-py3-none-any.whl.
-Then you can install the TF_Adapter plug-in using pip.
+#### Install the plugin package.
+Decompress the **tfadapter.tar** file to generate **npu_bridge-1.15.0-py3-none-any.whl**.
+Then you can install the TF_Adapter plugin using pip.
 ```
 pip install ./dist/python/dist/npu_bridge-1.15.0-py3-none-any.whl
 ```
 
-It should be noted that you should ensure that the installation path is the same as the Python you specified when compiling
-The interpreter search path is consistent.
+Note: Ensure that the installation path is consistent with the Python interpreter search path you specified at compile time.
 
-## Contributing
+## Contribution
 
-Welcome to contribute.
+Welcome your contributions.
 
 ## Release Notes
 
-For Release Notes, see our [RELEASE](RELEASE.md).
+For *Release Notes*, see our [RELEASE](RELEASE.md).
 
 ## License