What is Hugging Face?
As already mentioned in our comprehensive glossary, Hugging Face is a company with an affiliated community. Hugging Face offers a variety of resources and tools, including pre-trained models and libraries for working with text data. In the following article, we want to take a closer look at a few steps that are appropriate for using Hugging Face correctly and gradually exploiting its full potential.
Getting started with Hugging Face
1. First things first... registration
Before you can really get started, it is necessary to create an account and install the Hugging Face library on your own computer.
2. Here we go: Experiment with pre-trained models
Hugging Face offers a wide range of pre-trained models for various NLP tasks (e.g., text classification, translation, entity recognition). These models are available immediately after installation and can be easily used to solve specific tasks or approach a possible solution.
3. Deeper commitment: fine-tuning models
After an initial exploratory phase, it is now also possible to enter your own data sets and prepare them for in-depth and specifically individual training and then use them.
4. Discuss! Make extensive use of community and additional resources
The opportunity to support each other, ask questions and thus fully expand your own horizons is a clear plus of using Hugging Face. No one needs to know everything; there is a lively exchange within the community, which is driven by honest interest and sustained desire to learn. For example, it is possible to solve confusing problems sustainably with the help of the forum function. Models pre-trained by other users can also be loaded and used in accordance with open-source ethics. For this purpose, Hugging Face also includes a steadily growing gallery of models.
5. Always a step ahead: documentation and tutorials
If you don't know what to do, we recommend the comprehensive tutorials and meticulous documentation, which are also available in abundance. Using relevant example projects, innovative methods with new data sets can be learned in a playful way.
conclusion
The procedures outlined here in brief only provide a rough overview. Ultimately, curious and decisive exploration is required to wrest this initial spark of real knowledge. There is so much more to discover and a lot of synergies to use. Ready for life-long learning? Join us too and help drive the discourse forward.
