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Introduction

Welcome to the 🤗 Course!

This course will teach you lot about natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. Information technology'south completely free and without ads.

What to expect?

Here is a brief overview of the course:

Brief overview of the chapters of the course.

Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the form, you will be familiar with how Transformer models work and will know how to utilize a model from the Hugging Face Hub, fine-melody it on a dataset, and share your results on the Hub!
Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers earlier diving into archetype NLP tasks. Past the terminate of this part, yous will be able to tackle the about mutual NLP problems by yourself.
Capacity 9 to 12 get across NLP, and explore how Transformer models can be used tackle tasks in speech processing and computer vision. Along the way, you'll larn how to build and share demos of your models, and optimize them for production environments. By the end of this part, yous will exist gear up to use 🤗 Transformers to (almost) any car learning problem!

This course:

Requires a skillful knowledge of Python
Is better taken after an introductory deep learning form, such every bit fast.ai's Practical Deep Learning for Coders or one of the programs developed by DeepLearning.AI
Does not wait prior PyTorch or TensorFlow noesis, though some familiarity with either of those will help

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 similar naive Bayes and LSTMs that are well worth knowing about!

Who are we?

Near the authors:

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

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

Sylvain Gugger is a Inquiry 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 principal focus of his enquiry is on making deep learning more than accessible, by designing and improving techniques that let models to railroad train fast on express resources.

Merve Noyan is a developer abet at Hugging Face, working on developing tools and edifice content around them to democratize automobile learning for anybody.

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

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

Leandro von Werra is a car learning engineer in the open-source team at Hugging Face up and also a co-author of the an upcoming O'Reilly book on Transformers. He has several years of industry experience bringing NLP projects to production by working across the whole car learning stack..

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

How to use the pipeline() office to solve NLP tasks such as text generation and classification
About the Transformer architecture
How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases