The Hugging Face Hub for Galleries, Libraries, Archives and Museums

The Hugging Face Hub for Galleries, Libraries, Archives and Museums

What is the Hugging Face Hub?

Hugging Face aims to make high-quality machine learning accessible to everyone. This goal is pursued in various ways, including developing open-source code libraries such as the widely-used Transformers library, offering free courses, and providing the Hugging Face Hub.

The Hugging Face Hub is a central repository where people can share and access machine learning models, datasets and demos. The Hub hosts over 190,000 machine learning models, 33,000 datasets and over 100,000 machine learning applications and demos. These models cover a wide range of tasks from pre-trained language models, text, image and audio classification models, object detection models, and a wide range of generative models.

The models, datasets and demos hosted on the Hub span a wide range of domains and languages, with regular community efforts to expand the scope of what is available via the Hub. This blog post intends to offer people working in or with the galleries, libraries, archives and museums (GLAM) sector to understand how they can use — and contribute to — the Hugging Face Hub.

You can read the whole post or jump to the most relevant sections!

• If you don't know what the Hub is, start with: What is the Hugging Face Hub?
• If you want to know how you can find machine learning models on the Hub, start with: How can you use the Hugging Face Hub: finding relevant models on the Hub
• If you want to know how you can share GLAM datasets on the Hub, start with Walkthrough: Adding a GLAM dataset to the Hub?
• If you want to see some examples, check out: Example uses of the Hugging Face Hub

What can you find on the Hugging Face Hub?

Models

The Hugging Face Hub provides access to machine learning models covering various tasks and domains. Many machine learning libraries have integrations with the Hugging Face Hub, allowing you to directly use or share models to the Hub via these libraries.