paces, _interactive apps for demonstrating ML models directly in your browser_ . 

The Hub offers **versioning, commit history, diffs, branches, and over a dozen
library integrations** ! You can learn more about the features that all
repositories share in the [ **Repositories documentation** ](./repositories) .

##  Models

You can discover and use dozens of thousands of open-source ML models shared
by the community. To promote responsible model usage and development, model
repos are equipped with [ Model Cards ](./model-cards) to inform users of each
model’s limitations and biases. Additional [ metadata ](./model-cards#model-
card-metadata) about info such as their tasks, languages, and metrics can be
included, with training metrics charts even added if the repository contains [
TensorBoard traces ](./tensorboard) . It’s also easy to add an [ **inference
widget** ](./models-widgets) to your model, allowing anyone to play with the
model directly in the browser! For programmatic access, an API is provided to
[ **instantly serve your model** ](./models-inference) .

To upload models to the Hub, or download models and integrate them into your
work, explore the [ **Models documentation** ](./models) . You can also choose
from [ **over a dozen libraries** ](./models-libraries) such as 🤗
Transformers, Asteroid, and ESPnet that support the Hub.

##  Datasets

The Hub is home to over 5,000 datasets in more than 100 languages that can be
used for a broad range of tasks across NLP, Computer Vision, and Audio. The
Hub makes it simple to find, download, and upload datasets. Datasets are
accompanied by extensive documentation in the form of [ **Dataset Cards**
](./model-cards) and [ **Dataset Preview** ](./datasets-overview#datasets-on-
the-hub) to let you explore the data directly in your browser. While many
datasets are public, [ **organizations** ](./organizations) and individuals
can create private datasets to comply with licensing or privacy issues. You
can learn more about [ **Datasets here on Hugging Face Hub documentation**
](./datasets-overview) .

The [ 🤗 ` datasets ` ](https://huggingface.co/docs/datasets/index) library
allows you to programmatically interact with the datasets, so you can easily
use datasets from the Hub in your projects. With a single line of code, you
can access the datasets; even if they are so large they don’t fit in your
computer, you can use streaming to efficiently access the data.

##  Spaces

[ Spaces ](https://huggingface.co/spaces) is a simple way to host ML demo apps
on the Hub. They allow you to build your ML portfolio, showcase your projects
at conferences or to stakeholders, and work collaboratively with other people
in the ML ecosystem.

We currently support two awesome Python SDKs ( **[ Gradio
](https://gradio.app/) ** and **[ Streamlit ](https://streamlit.io/) ** ) that
let you build cool apps in a matter of minutes. Users can also create static
Spaces which are simple HTML/CSS/JavaScript page within a Space.

After you’ve explored a few Spaces (take a look at our [ Space of the Week!
](https://huggingface.co/spaces) ), dive into the [ **Spaces documentation**
](./spaces-overview) to learn all about how you can create your own Space.
You’ll also be able to upgrade your Space to run on a GPU or other accelerated
hardware. ⚡️

##  Organizations

Companies, universities and non-profits are an essential part of the Hugging
Face community! The Hub offers [ **Organizations** ](./organizations) , which
can be used to group accounts and manage datasets, models, and Spaces.
Educators can also create collaborative organizations for students using [
Hugging Face for Classrooms ](https://huggingface.co/classrooms) . An
organization’s repositories will be featured on the organization’s page and
every member of the organization will have the ability to contribute to the
repository. In addition to conveniently grouping all of an organization’s
work, the Hub allows admins to set roles to [ **control access to