<aside> 👋 Welcome!
We’ve assembled a toolkit that university instructors can use to easily prepare labs, homework, or classes. The content is designed in a self-contained way such that it can easily be incorporated into the existing curriculum. This content is free and uses widely known Open Source technologies (transformers, gradio, etc).
Alternatively, you can request for someone on the Hugging Face team to run the tutorials for your class via the ML demo.cratization tour initiative!
Apart from tutorials, we also share other resources to go further into ML or that can assist in designing course content.
</aside>
🇪🇸 Click here to access the Spanish translated version of the Education Toolkit
In this tutorial, you get to:
- Explore the over 30,000 models shared in the Hub.
- Learn efficient ways to find the right model and datasets for your own task.
- Learn how to contribute and work collaboratively in your ML workflows
Duration: 20-40 minutes 👉 click here to access the tutorial or 👩🏫 the lecture slides
In this tutorial, you get to:
- Explore ML demos created by the community.
- Build a quick demo for your machine learning model in Python using the
gradiolibrary- Host the demos for free with Hugging Face Spaces
- Add your demo to the Hugging Face org for your class or conference
Duration: 20-40 minutes
👉 click here to access the tutorial or 👩🏫 the lecture slides
In this tutorial, you get to:
- Transformer neural networks can be used to tackle a wide range of tasks in natural language processing and beyond.
- Transfer learning allows one to adapt Transformers to specific tasks.
- The
pipeline()function from thetransformerslibrary can be used to run inference with models from the Hugging Face Hub.This tutorial is based on the first of our O'Reilly book Natural Language Processing with Transformers - check it out if you want to dive deeper into the topic!
Duration: 30-45 minutes
<aside> ✉️ If you have any questions, please contact [email protected]!
</aside>