Hugging Face is a platform and community space providing resources for creating, training and deploying machine learning models based on open source technologies and code. It is also a meeting place for artificial intelligence (AI) researchers, engineers and enthusiasts to share ideas, get support and participate in open source projects.

Hugging Face

What is HuggingFace for?

The HuggingFace Transformer Library was designed to provide ease of use, flexibility, and simplicity in using complex models with an architecture similar to the one mentioned above, through a single API. Models can be loaded, trained and saved without difficulty. Initially, HuggingFace was primarily used for Natural Language Processing (NLP) use cases, but has since evolved to encompass audio and visual use cases. This works as a typical deep learning solution with several steps, from data acquisition to fine-tuning a model, allowing a reusable domain-by-domain workflow.

The importance of community in AI development

With the recent emergence of flexible and hybrid work practices, we are seeing a growing adoption of tools that enable data science teams and experts to collaborate remotely. The open source community is playing an increasingly crucial role in the advancement of AI. Hugging Face addresses this need by providing a central hub where anyone can share and explore models and datasets, with the goal of democratizing AI for everyone.

First steps with Hugging Face

Creating a repository

By signing up for Hugging Face, you get a Git-based hosted repository to store your models, datasets and spaces. Signing up as an individual contributor is free, and “Pro” plans and pricing models for organizations are also available.


A template is basically a Git repository dedicated to files related to a machine learning model you want to share, offering all the classic benefits such as versioning, branching and discoverability.


To create a new dataset, you need to follow a similar process as creating a new model: specify the name, license type and public or private access. Then you get a view of the repository, including a “dataset map” and “files and versions”.


Spaces provide a place to showcase your work as standalone machine learning demo applications, which is ideal for building a portfolio of your projects.

Explore the community

In addition to your own (or your organization’s) repository, you can browse the tens of thousands of models, datasets and spaces contributed by the Hugging Face community.


  1. What is Hugging Face? Hugging Face is a platform and community providing tools to build, train and deploy machine learning models based on open source technologies and code.
  2. Why is the community important to Hugging Face? Community plays a major role in advancing AI by allowing researchers, engineers, and enthusiasts to collaborate and exchange ideas, support, and contribute to open source projects.
  3. How do I get started with Hugging Face? Sign up for Hugging Face to get a Git-based hosted repository and start storing models, datasets and spaces.
  4. What are the benefits of using Hugging Face? Hugging Face provides a centralized space to share and explore models and datasets, facilitating collaboration among community members and helping to democratize AI for everyone.


Hugging Face is an essential platform for AI enthusiasts, providing a space to develop and share machine learning models, datasets and spaces. By joining this vibrant community, you can contribute to AI advancements, exchange ideas and get support for your projects. Feel free to sign up and explore all that Hugging Face has to offer.

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