fix typo

Signed-off-by: Wei-Chun, Chang <wcchang@infuseai.io>

Author: wcchang1115

README.md

@@ -8,7 +8,7 @@ Try it out from the [Getting Started](https://artivc.io/usage/getting-started/)
 
 # Features
 
-- **Data Versioning**: Version your data like versioning code. ArtiVC supports commit history, commit message, and version tag. You can diff two commits, and pull data from the speciifc version.
+- **Data Versioning**: Version your data like versioning code. ArtiVC supports commit history, commit message, and version tag. You can diff two commits, and pull data from the specific version.
 - **Use your own storage**: We are used to putting large files in NFS or S3. To use ArtiVC, you can keep putting your files on the same storage without changes.
 - **No additional server is required**: ArtiVC is a CLI tool. No server or gateway is required to install and operate.
 - **Multiple backends support**:  ArtiVC natively supports local filesystem, remote filesystem (by SSH), AWS S3, Google Cloud Storage, and Azure Blob Storage as backend. And 40+ backends are supported through [Rclone](https://artivc.io/backends/rclone/) integration. [Learn more](https://artivc.io/backends/)

docs/content/en/_index.md

@@ -23,7 +23,7 @@ geekdocAnchor: false
 {{< columns >}}
 ### Data Versioning
 
-Version your data like versioning code. ArtiVC supports commit history, commit message, and version tag. You can diff two commits, and pull data from the speciifc version.
+Version your data like versioning code. ArtiVC supports commit history, commit message, and version tag. You can diff two commits, and pull data from the specific version.
 
 <--->
 

docs/content/en/usage/dryrun.md

@@ -3,7 +3,7 @@ title: Dry Run
 weight: 11
 ---
 
-Pushing and pulling data is time-consuming. And need to be double-check before transfering. Dry run is the feature allows to list the changeset before sending.
+Pushing and pulling data is time-consuming. And need to be double-checked before transferring. Dry-run is the feature that allows listing the changeset before sending.
 
 
 ## Push

docs/content/en/usage/expose.md

@@ -3,7 +3,7 @@ title: Expose the data
 weight: 20
 ---
 
-ArtiVC repository can be exposed as a http endpoint. In S3, we can just make the bucket and give the data consumer the http endpiont of the repository. In this way, we can download data through CDN or other reverse proxies.
+ArtiVC repository can be exposed as an HTTP endpoint. In S3, we can just make the bucket and give the data consumer the HTTP endpoint of the repository. In this way, we can download data through CDN or other reverse proxies.
 
 1. [Make your S3 bucket public](https://aws.amazon.com/premiumsupport/knowledge-center/read-access-objects-s3-bucket/?nc1=h_ls)
 1. Copy the public URL of your repository. For example

docs/content/en/usage/getting-started.md

@@ -182,7 +182,7 @@ For more information, please see the [Azure Blob Storage](../../backends/azurebl
    avc log
    ```
 
-## Clone data from exisiting repository
+## Clone data from existing repository
 1. Go to the folder to clone repository
 
    {{<tabs "avc clone">}}

docs/content/en/use-cases/dataprep.md

@@ -5,7 +5,7 @@ weight: 2
 
 Organizing dataset can be a hassle, especially as data is constantly evolving. ArtiVC is the most suitable tool to organize the dataset. There are the following benefits.
 
-- No need to transfer files with the exisitng content. Even you rename or copy to different folder. ArtiVC knows they are the same content. It is common to move or keep the same images, videos when the dataset is evloving.
+- No need to transfer files with the existing content. Even you rename or copy to different folder. ArtiVC knows they are the same content. It is common to move or keep the same images, videos when the dataset is evolving.
 - Version tagging. If there is a stable version of dataset, we can tag a commit as the human-readable version.
 
 ## Prepare a dataset
@@ -29,7 +29,7 @@ Here are the common steps to prepare a dataset
    # Push
    avc push -m 'my second version' 
    ```
-1. If there are new version is pushed by others, sync the data set with remote
+1. If there are new versions is pushed by others, sync the data set with remote
    ```shell
    # Check the difference
    avc pull --dry-run