docs: update introduction

opendatalab · Nov 17, 2023 · 12f61ed · 12f61ed
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diff --git a/src/pages/guide.account/markdown_en-US.mdx b/src/pages/guide.account/markdown_en-US.mdx
@@ -1,15 +1,15 @@
 import xlabAcount from './xlab-account-cn.png';
 import localAcount from './local-account-cn.png';
 
-## Registration and Login
+## Account
 
-### Web online version
+### Online
 
 You can register and log in using "Mobile Verification Code", or you can also register and log in with "Mobile Number/Email + Password".
 
 <img src={xlabAcount} />
 
-### Local deployment version
+### Local deployment
 
 You can log in by registering with an email.
 

diff --git a/src/pages/guide.install/markdown_en-US.mdx b/src/pages/guide.install/markdown_en-US.mdx
@@ -2,7 +2,7 @@
 
 1. Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html)，choose the corresponding operating system type and download it for installation.
 
-> **Note：** If your system is MacOS with an Intel chip, please install [intel x86_64版本的Miniconda](https://repo.anaconda.com/miniconda/)。
+> **Note：** If your system is MacOS with an Intel chip, please install [Miniconda of intel x86_64](https://repo.anaconda.com/miniconda/)。
 
 2. After the installation is complete, run the following command in the terminal (you can choose the default 'y' for prompts during the process):
 
@@ -24,7 +24,7 @@ conda activate labelu
 pip install labelu
 ```
 
-> To install the test version：`pip install --extra-index-url https://test.pypi.org/simple/ labelu==<测试版本号>`
+> To install the test version：`pip install --extra-index-url https://test.pypi.org/simple/ labelu==<test revision>`
 
 5. Run LabelU：
 

diff --git a/src/pages/guide.introduction/markdown_en-US.mdx b/src/pages/guide.introduction/markdown_en-US.mdx
@@ -4,7 +4,7 @@
 
 LabelU is an open-source data annotation tool that can help users quickly, accurately, and efficiently annotate data, thereby improving the performance and quality of machine learning models. LabelU supports various types of annotations, including label classification, bounding boxes, polygons, points, lines, text descriptions, etc., meeting different scenarios and annotation task needs.
 You can experience the product in two ways:
-- Online experience：[https://labelu.shlab.tech/](https://labelu.shlab.tech/)
+- Try online [https://labelu.shlab.tech/](https://labelu.shlab.tech/)
 - Local deployment：[https://opendatalab.github.io/labelU/#/guide/install](https://opendatalab.github.io/labelU/#/guide/install)
 
 ### Feature Introduction
@@ -13,7 +13,7 @@ LabelU provides a variety of annotation tools and features, supporting image, vi
 
 - Image-based: Multifunctional image processing tools, covering 2D box, semantic segmentation, polyline, key points, and various other annotation tools，to assist in the identification, annotation, and analysis of images.
 - Video-based: With powerful video processing capabilities, it can perform video segmentation, video classification, video information extraction, and other functions, providing high-quality annotated data for model training.
-- Audio-based: Efficient and accurate audio analysis tools, capable of audio segmentation, audio classification, audio information extraction, and other functions, making complex sound information intuitively visualized. 
+- Audio-based: Efficient and accurate audio analysis tools, capable of audio segmentation, audio classification, audio information extraction, and other functions, making complex sound information intuitively visualized.
 
 ### Concept Introduction