What is Hugging Face Why Use Hugging Face and What is it used for?

Hugging Face is an open source platform that provides tool and resources for working on NLP and computer vision projects.Hugging face is a popular in the machine learning community due to its stress on community collaboration, efficiency and chance to build a professional portfolio.The platform offers tokenizers, machine learning datasets model hosting for training and Implementing AI models.Hugging Face is a community and data science platform that provides

  • A place where a vast community of ML engineers, data scientists and researcher can come together and share ideas contribute to open source codes and get help.
  • Tools that assist users to deploy ML models, train and build open source technologies and codes.

What is Hugging Face?

Hugging Face is a company and open source company focused on the field of AI.It provide a platform to collaborate, learn and share work in NLP and computer vision.Hugging Face aims to provide people with all necessary tools, resources and libraries needed to work on NLP models for their benefit. It start in 2016 came from emoji hugging face to look caring.It changes from chatbot app to mobile app.Hugging Face is more than an emoji.It perform as a hub for AI experts such as AI or GitHub.Hugging Face is an open source machine learning platform and data science.It published as chatbot app for teenagers in 2017. Hugging face developed after several years to be a place where you can host your own AI models, collaborate with your team and train them.You can also search and use models generated by other person look for and use datasets, test demo projects.Hugging Face is an open source platform to solve AI. Hugging Face provide the tool to involve several people in shaping the artificial intelligent tools of the future.

Hugging Face provides the infrastructure to run, deploy and demo artificial intelligence in live apps. Hugging Face is known as GitHub of machine learning because it allows developers to test and share their works.Users can also search data sets and models that other peoples have uploaded.Hugging Face is popular for its Transformers Python Library which simplifies the process of training ML models and downloading.The Library helps developers to include to include one of the ML models hosted on Hugging Face in their workflow and create ML PipeLines.

How is Hugging Face used?

Hugging Face is an AI supporting community and platform.The community used Hugging Face for following tasks

Host Demos

Hugging Face allow users test and showcase models more simply.This allow users in browser demo of machine learning models and generate interaction.

Discover and Share Data Sets

Developers can discover data sets to train their models and share data sets for training machine learning models through the dataset library.

Evaluate ML Models

Hugging Face provides access to a code library for data sets and evaluating machine learning models.

Implement machine learning models

User can upload machine learning model to the platform.There are models for variety of tasks like computer vision, audio and image generation.

Develop business applications

Hugging Face Enterprises Hub allow business user in a privately hosted enviroment with data sets and open source libraries.


Hugging Face has been involved in research projects mission to progress the field of NLP.The site also has a page where only research papers are listed.

Benefits of using Hugging Face

Hugging Face with its open nature and communal behavior provides the following facilities to end users


Hugging Face provides access to a huge community, continously upgraded tutorials, documentation and models.


Hugging Face allows swift prototyping, deployment of ML and NLP apps.

Save Money

Hugging Face provides scalable solution for businesses.It is less in cost.Building huge models from zero can be costly and using Hugging Face hosted model is save your penny.


Hugging Face facilitate end user with pre trained models,, APIs and scripts for deployment of ML and NLP apps.


Hugging Face assist users integrate multiple ML frameworks.

Why use Hugging Face?

Hugging Face is most popular in the machine learning society for various reasons.One of superb feature of Hugging Face is the ability to generate your own AI models.This model will be hosted on the platform facilitate you to upload all necessary files, keep track of version, insert more data about it.You can manage the model with your models publics or private.You can determined when to launch them to the universe or not. It also help you to generate discussion directly on the model page which is good for collaborating with others and managing pull requests.You do not need to host the model in another platform you can initiate it directly from Hugging Face send requests and pull the outputs into any app.The platform is very important due to its deployment tools and open source nature. It permits user to share model,resources and research and to decrease resource consumption, model training time. You can browse from Hugging Face model if you are new one.There are more 200,000 models available.


Hugging Face assist democratize NLP by providing access to pre trained model researchers, businesses and developers.


Provide all necessary tools and documentation to initiate training and building model in one platform reducing the complexity of model development.

Professional Portfolio

You can create a professional portfolio in Hugging Face and earn a AI related Jobs, training and development.

Community Collaboration

The open source nature of hugging face provides a platform that encourages collaboration and knowledge sharing in machine learning community.

How to sign up for Hugging Face

Hugging Face is free to sign up for as a community user.User get a repository where they can store datasets, spaces and models.Hugging Face also offers a paid pro account that give user access to more facilities and business account at a much higher rate.The business account adds more customer support, accessibility and security. After creating an account user can do the following;

  • Look the activity feed
  • Access the Hugging Face Hub
  • Create private repositories or organization
  • Explore their profile and adjust settings
  • Start a Space, Dataset or Model
  • Discover the latest trend within Hugging Face community
  • Access useful ML documentation and resources

Hugging Face Features

Hugging Face Hub features are following;


Machine learning model on their technical knowledge to implement and use.Hugging Face provides the computing resources to host demos.Spaces does not require any technical knowledge to use.Some examples of Hugging Face spaces include the following

Image to Story

User can upload an image and LLM uses text generation to write a story based on it.


Hugging Face hosts a huge library models.There are more than three lacs models on Hugging Face.Hugging Face also host open source ML models on the platform.


Datasets assist train model to understand relationship between data.Hugging Face facilitate user to access data sets uploaded by the community. imdb is one example which contain a collection of movie reviews and wikipedia which contain data from wikipedia.

What is Hugging Face Used For?


Hugging Face provide all essential tools and models.This would include tutorial and documentation how to use tools and train model from start to finish.


Hugging Face provides tokenizers to break down data into smaller units.The platform offers tokenization libraries for various languages making it easier to prepare text data.


Hugging Face allow members to download datasets for anyone to improve and use this project.Hugging Face play a key role in the advancement of NLP technology.The platform provide all the assets, tools documentation to learn machine learning for specific needs.

Hugging Face and AI ecosystem

Hugging Faces introduces a friendly method to AI progress in contrast with other AI businesses.Hugging Face will provide AI models to the developers.Hugging Face main goal is to provide access to common users.


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