Fix typos

docs/hub/academia-hub.md

@@ -44,4 +44,4 @@ Ask your university's IT or Procurement Team to <a href="https://huggingface.co/
 
 #### Private storage
 
-Academia Hub includes 1TB of [private repository storage](./storage-limits) per seat in the subscription, i.e. if your institution has 400 students and faculty, your students and faculty have 400TB included storage accross their private models and datasets.
+Academia Hub includes 1TB of [private repository storage](./storage-limits) per seat in the subscription, i.e. if your institution has 400 students and faculty, your students and faculty have 400TB included storage across their private models and datasets.

docs/hub/datasets-image.md

@@ -78,7 +78,7 @@ file_name,text
 1.jpg,a drawing of a green pokemon with red eyes
 2.jpg,a green and yellow toy with a red nose
 3.jpg,a red and white ball with an angry look on its face
-4.jpg,a cartoon ball with a smile on it's face
+4.jpg,a cartoon ball with a smile on its face
 ```
 
 You can also use a [JSONL](https://jsonlines.org/) file `metadata.jsonl`:
@@ -87,7 +87,7 @@ You can also use a [JSONL](https://jsonlines.org/) file `metadata.jsonl`:
 {"file_name": "1.jpg","text": "a drawing of a green pokemon with red eyes"}
 {"file_name": "2.jpg","text": "a green and yellow toy with a red nose"}
 {"file_name": "3.jpg","text": "a red and white ball with an angry look on its face"}
-{"file_name": "4.jpg","text": "a cartoon ball with a smile on it's face"}
+{"file_name": "4.jpg","text": "a cartoon ball with a smile on its face"}
 ```
 
 And for bigger datasets or if you are interested in advanced data retrieval features, you can use a [Parquet](https://parquet.apache.org/) file `metadata.parquet`.

docs/hub/models-inference.md

@@ -7,7 +7,7 @@ Please refer to the [Inference Providers Documentation](https://huggingface.co/d
 HF-Inference API is one of the many providers available on the Hugging Face Hub.
 It is deployed by Hugging Face ourselves, using text-generation-inference for LLMs for instance. This service used to be called “Inference API (serverless)” prior to Inference Providers. 
 
-For more details about the HF-Inference API, check out it's [dedicated page](https://huggingface.co/docs/inference-providers/providers/hf-inference).
+For more details about the HF-Inference API, check out its [dedicated page](https://huggingface.co/docs/inference-providers/providers/hf-inference).
 
 ## What technology do you use to power the HF-Inference API?
 

docs/hub/models-widgets.md

@@ -188,6 +188,6 @@ inference:
     temperature: 0.7
 ``` 
 
-Inference Providers allows you to send HTTP requests to models in the Hugging Face Hub programatically. It is an abstraction layer on top of External providers. ⚡⚡ Learn more about it by reading the [
+Inference Providers allows you to send HTTP requests to models in the Hugging Face Hub programmatically. It is an abstraction layer on top of External providers. ⚡⚡ Learn more about it by reading the [
 Inference Providers documentation](/docs/inference-providers). 
 Finally, you can also deploy all those models to dedicated [Inference Endpoints](https://huggingface.co/docs/inference-endpoints).

docs/hub/security-gpg.md

@@ -24,7 +24,7 @@ You can sign your commits locally using [GPG](https://gnupg.org/).
 Then configure your profile to mark these commits as **verified** on the Hub,
 so other people can be confident that they come from a trusted source.
 
-For a more in-depth explanation of how git and GPG interact, please visit the the [git documentation on the subject](https://git-scm.com/book/en/v2/Git-Tools-Signing-Your-Work)
+For a more in-depth explanation of how git and GPG interact, please visit the [git documentation on the subject](https://git-scm.com/book/en/v2/Git-Tools-Signing-Your-Work)
 
 Commits can have the following signing statuses:
 

docs/hub/spaces-sdks-docker-shiny.md

@@ -51,7 +51,7 @@ This file contains all of your application logic. If you prefer, you can break t
 
 _Dockerfile_
 
-The Dockerfile builds off of the the [rocker shiny](https://hub.docker.com/r/rocker/shiny) image. You'll need to modify this file to use additional packages. 
+The Dockerfile builds off of the [rocker shiny](https://hub.docker.com/r/rocker/shiny) image. You'll need to modify this file to use additional packages. 
 If you are using a lot of tidyverse packages we recommend switching the base image to [rocker/shinyverse](https://hub.docker.com/r/rocker/shiny-verse).
 You can install additional R packages by adding them under the `RUN install2.r` section of the dockerfile, and github packages can be installed by adding the repository under `RUN installGithub.r`.
 

docs/hub/spaces-sdks-streamlit.md

@@ -16,7 +16,7 @@ You can edit the `sdk_version`, but note that issues may occur when you use an u
 For in-depth information about Streamlit, refer to the [Streamlit documentation](https://docs.streamlit.io/).
 
 <Tip warning={true}>
-Only port 8501 is allowed for Streamlit Spaces (default port). As a result if you provide a `config.toml` file for your Space make sure the default port is not overriden.
+Only port 8501 is allowed for Streamlit Spaces (default port). As a result if you provide a `config.toml` file for your Space make sure the default port is not overridden.
 </Tip>
 
 ## Your First Streamlit Space: Hot Dog Classifier