👋 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.
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
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
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
We provide a course (free and without ads) that teaches you about natural language processing (NLP) using libraries from the Hugging Face ecosystem.
👉 click here to access the 🤗 Course
💡 This course:- Requires good knowledge of Python
- Is better taken after an introductory deep learning course, such as fast.ai’s Practical Deep Learning for Coders or one of the programs developed by DeepLearning.AI
- Does not expect prior PyTorch or TensorFlow knowledge, though some familiarity with either of those will help
Released February 2022
From experts at Hugging Face, learn all about Transformers and their applications to a wide range of NLP tasks.
👉 click here to visit the book’s website
💡 This book:- Is written for data scientists and machine learning engineers who may have heard about the recent breakthroughs involving transformers, but are lacking an in-depth guide to help them adapt these models to their own use cases.
- Assumes you have some practical experience with training models on GPUs.
- Does not expect prior PyTorch or TensorFlow knowledge, though some familiarity with either of those will help