Tutorials
🎓Introducing the Hugging Face Course 🎓
This new course is aimed at beginners with the Hugging Face ecosystem, and showcases how to use libraries such as 🤗 Transformers, 🤗 Tokenizers, 🤗 Datasets and 🤗 Accelerate. Students following this course will understand how to approach (almost) any NLP problem and benefit from all the past experience of the community to solve it thanks to the Hugging Face Hub.
The course consists of three parts and is entirely free! Head over to huggingface.co/course to get started!
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4️⃣First chapters released today!
Today we’re super excited to launch the first 4 chapters of the course.
By following the first chapters of the course, you will learn what Transformer models are, how to train them to tackle a wide variety of NLP problems, and how to contribute back to the Hugging Face community.
We are also super excited to welcome you to live sessions, led by one of the course creators. We are organizing two sessions per chapter, so you can pick the one that works best for your time zone. Check out the session hours below and save the dates!
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Events & Talks
📙Chapter 1: Transformer models
This chapter will teach you about Transformer models as well as the pipeline method and how to apply it to NLP tasks such as text generation, text classification, question answering, and many others. Finally, you will learn about encoders, decoders, and encoder-decoder models.
Join either live session to cover Chapter 1 with us!
Chapter 1 with Lysandre (Twitter/LinkedIn): Wednesday, June 16th (8:00-9:00 UTC)
Chapter 1 with Sylvain (Twitter/LinkedIn): Thursday, June 17th (18:00-19:00 UTC)
Check out the course introduction!
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📘Chapter 2: Using 🤗 Transformers
This chapter will teach you about using models and tokenizers on their own. You'll learn what tokenizers are and how they can be used to convert text to inputs a model can understand; and what models are and how they can be used to obtain state-of-the-art predictions.
Join either live session to cover Chapter 2 with us!
Chapter 2, with Lewis (Twitter/LinkedIn): Wednesday, June 23rd (8:00-9:00 UTC)
Chapter 2, with Sylvain (Twitter/LinkedIn): Thursday, June 24th (18:00-19:00 UTC)
Checkout the videos on the pipeline: What happens inside the pipeline function? (TensorFlow), What happens inside the pipeline function? (PyTorch)
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📗Chapter 3: Fine-tuning a pretrained model
This chapter will teach you all about training NLP models. Two versions are available; one using PyTorch, showing the Trainer API and the Hugging Face Accelerate library; and the other one using TensorFlow, showing how to leverage NLP models using Keras.
Join either live session to cover Chapter 3 with us!
Chapter 3 — PyTorch, with Lewis (Twitter/LinkedIn): Wednesday, June 30th (8:00-9:00 UTC)
Chapter 3 — PyTorch, with Sylvain (Twitter/LinkedIn): Thursday, July 1st (18:00-19:00 UTC)
Chapter 3 — TensorFlow, with Matt (Twitter): Wednesday, June 30th (17:00-18:00 UTC)
Chapter 3 — TensorFlow, with Matt (Twitter): Thursday, July 1st (10:00-11:00 UTC)
Checkout the datasets overview videos: Hugging Face Datasets overview (PyTorch), Hugging Face Datasets overview (Tensorflow)!
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📕Chapter 4: Sharing models and tokenizers
This chapter focuses on the community aspect of the Hugging Face Ecosystem: from using models trained by community members, to contributing your own, with the appropriate documentation.
Join either live session to cover Chapter 4 with us!
Chapter 4, with Omar 3 (Twitter/LinkedIn): Wednesday, July 7th (8:00-9:00 UTC)
Chapter 4, with Omar 2 (Twitter/LinkedIn): Thursday, July 8th (18:00-19:00 UTC)
Check out the Navigating the Model Hub video!
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👩🏫May the Course be with You!
Any questions? Head out on the dedicated category on the forum, there is one topic per chapter where you can find help, and other topics to help you form a virtual study group with other users, to learn faster together!
Do you think your company would benefit from the course through a training workshop?
We offer 2-day workshops to introduce AI teams to Transformers with a Hugging Face instructor, leveraging the Course to discuss the practical application of Transformers to your business use cases.