Introduction

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Welcome to the 🤗 Course!

This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub.

We’ll also cover libraries outside the Hugging Face ecosystem. These are amazing contributions to the AI community and incredibly useful tools.

It’s completely free and without ads.

Understanding NLP and LLMs

While this course was originally focused on NLP (Natural Language Processing), it has evolved to emphasize Large Language Models (LLMs), which represent the latest advancement in the field.

What’s the difference?

Throughout this course, you’ll learn about both traditional NLP concepts and cutting-edge LLM techniques, as understanding the foundations of NLP is crucial for working effectively with LLMs.

What to expect?

Here is a brief overview of the course:

Brief overview of the chapters of the course.

This course:

After you’ve completed this course, we recommend checking out DeepLearning.AI’s Natural Language Processing Specialization, which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!

Who are we?

About the authors:

Abubakar Abid completed his PhD at Stanford in applied machine learning. During his PhD, he founded Gradio, an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead.

Ben Burtenshaw is a Machine Learning Engineer at Hugging Face. He completed his PhD in Natural Language Processing at the University of Antwerp, where he applied Transformer models to generate children stories for the purpose of improving literacy skills. Since then, he has focused on educational materials and tools for the wider community.

Matthew Carrigan is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we’re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless.

Lysandre Debut is a Machine Learning Engineer at Hugging Face and has been working on the 🤗 Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API.

Sylvain Gugger is a Research Engineer at Hugging Face and one of the core maintainers of the 🤗 Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote Deep Learning for Coders with fastai and PyTorch with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources.

Dawood Khan is a Machine Learning Engineer at Hugging Face. He’s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face.

Merve Noyan is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone.

Lucile Saulnier is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience.

Lewis Tunstall is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O’Reilly book Natural Language Processing with Transformers.

Leandro von Werra is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O’Reilly book Natural Language Processing with Transformers. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack..

FAQ

Here are some answers to frequently asked questions:

Link to the Hugging Face forums

Note that a list of project ideas is also available on the forums if you wish to practice more once you have completed the course.

Link to the Hugging Face course notebooks

The Jupyter notebooks containing all the code from the course are hosted on the huggingface/notebooks repo. If you wish to generate them locally, check out the instructions in the course repo on GitHub.

@misc{huggingfacecourse,
  author = {Hugging Face},
  title = {The Hugging Face Course, 2022},
  howpublished = "\url{https://huggingface.co/course}",
  year = {2022},
  note = "[Online; accessed <today>]"
}

Languages and translations

Thanks to our wonderful community, the course is available in many languages beyond English 🔥! Check out the table below to see which languages are available and who contributed to the translations:

Language Authors
French @lbourdois, @ChainYo, @melaniedrevet, @abdouaziz
Vietnamese @honghanhh
Chinese (simplified) @zhlhyx, petrichor1122, @yaoqih
Bengali (WIP) @avishek-018, @eNipu
German (WIP) @JesperDramsch, @MarcusFra, @fabridamicelli
Spanish (WIP) @camartinezbu, @munozariasjm, @fordaz
Persian (WIP) @jowharshamshiri, @schoobani
Gujarati (WIP) @pandyaved98
Hebrew (WIP) @omer-dor
Hindi (WIP) @pandyaved98
Bahasa Indonesia (WIP) @gstdl
Italian (WIP) @CaterinaBi, @ClonedOne, @Nolanogenn, @EdAbati, @gdacciaro
Japanese (WIP) @hiromu166, @younesbelkada, @HiromuHota
Korean (WIP) @Doohae, @wonhyeongseo, @dlfrnaos19
Portuguese (WIP) @johnnv1, @victorescosta, @LincolnVS
Russian (WIP) @pdumin, @svv73
Thai (WIP) @peeraponw, @a-krirk, @jomariya23156, @ckingkan
Turkish (WIP) @tanersekmen, @mertbozkir, @ftarlaci, @akkasayaz
Chinese (traditional) (WIP) @davidpeng86

For some languages, the course YouTube videos have subtitles in the language. You can enable them by first clicking the CC button in the bottom right corner of the video. Then, under the settings icon ⚙️, you can select the language you want by selecting the Subtitles/CC option.

Activating subtitles for the Hugging Face course YouTube videos
Don't see your language in the above table or you'd like to contribute to an existing translation? You can help us translate the course by following the instructions here.

Let’s go 🚀

Are you ready to roll? In this chapter, you will learn:

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