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  • Natural Language Processing with Transformers: Building Language Applications with Hugging Face

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Natural Language Processing with Transformers: Building Language Applications with Hugging Face

by Lewis Tunstall (Author), Leandro von Werra (Author), Thomas Wolf (Author) Format: Paperback
4.5 out of 5 stars (171)

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Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.

Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.

  • Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering
  • Learn how transformers can be used for cross-lingual transfer learning
  • Apply transformers in real-world scenarios where labeled data is scarce
  • Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization
  • Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

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  1. ISBN-10
    9355420323
  2. ISBN-13
    978-9355420329
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    March 1, 2022
  6. Language
    English
  7. Dimensions
    7 x 1 x 9.25 inches
  8. Print length
    406 pages
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  5. Sharing the knowledge of experts

    O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

From the Publisher

Who Is This Book For?

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. The book is not meant to be an introduction to machine learning, and we assume you are comfortable programming in Python and has a basic understanding of deep learning frameworks like PyTorch and TensorFlow. We also assume you have some practical experience with training models on GPUs. Although the book focuses on the PyTorch API of Transformers, Chapter 2 shows you how to translate all the examples to TensorFlow.

This book has been revised

  • See the revised edition
  • ISBN: 9781098136796
  • ASIN: 1098136799 (or Kindle B0B2FKYVNL)

The following resources provide a good foundation for the topics covered in this book. We assume your technical knowledge is roughly at their level:

  • Hands-On Machine Learning with Scikit-Learn and TensorFlow, by Aurélien Géron (O’Reilly)
  • Deep Learning for Coders with fastai and PyTorch, by Jeremy Howard and Sylvain Gugger (O’Reilly)
  • Natural Language Processing with PyTorch, by Delip Rao and Brian McMahan (O’Reilly)

Editorial Reviews

Review

"The preeminent book for the preeminent transformers library—a model of clarity!"
- Jeremy Howard, cofounder of fast.ai and professor at University of Queensland

"A wonderfully clear and incisive guide to modern NLP's most essential library. Recommended!"
- Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Stanford University; Director, Stanford Artificial Intelligence Laboratory (SAIL)

About the Author

Lewis Tunstall is a machine learning engineer at Hugging Face. He has built machine learning applications for startups and enterprises in the domains of NLP, topological data analysis, and time series. Lewis has a PhD in theoretical physics and has held research positions in Australia, the USA, and Switzerland. His current work focuses on developing tools for the NLP community and teaching people to use them effectively.

Leandro von Werra is a machine learning engineer in the open source team at Hugging Face. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack, and is the creator of a popular Python library called TRL, which combines transformers with reinforcement learning.

Thomas Wolf is chief science officer at and cofounder of Hugging Face. His team is on a mission to catalyze and democratize NLP research. Prior to cofounding Hugging Face, Thomas earned a PhD in physics and later a law degree. He has worked as a physics researcher and a European patent attorney.

Product details

  • ASIN ‏ : ‎ 1098103246
  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ March 1, 2022
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 406 pages
  • ISBN-10 ‏ : ‎ 9355420323
  • ISBN-13 ‏ : ‎ 978-9355420329
  • Item Weight ‏ : ‎ 1.55 pounds
  • Dimensions ‏ : ‎ 7 x 1 x 9.25 inches
  • Best Sellers Rank: #2,739,444 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.5 out of 5 stars (171)

About the authors

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  1. Thomas Wolf

    Thomas Wolf

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    Full content visible, double tap to read brief content.

    Thomas Wolf is Chief Science Officer and co-founder of Hugging Face Inc. He and his teams are on a mission to catalyse and democratise responsible ML and AI research by creating large scale open-source and open-science projects. Prior to founding HuggingFace, he gained a Ph.D. in statistical and quantum physics, and later a law degree from Sorbonne University. He previously worked as a physics researcher and a European Patent Attorney in the USA, France, and the Netherlands where he currently reside with his family.

    See more on the author's page
  2. Leandro von Werra

    Leandro von Werra

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Leandro von Werra is a machine learning engineer in the open source team at Hugging Face. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack, and is the creator of a popular Python library called TRL that combines transformers with reinforcement learning.

    See more on the author's page
  3. Aurélien Géron

    Aurélien Géron

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.

    Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

    A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.

    See more on the author's page
  4. Lewis Tunstall

    Lewis Tunstall

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Lewis Tunstall is a machine learning engineer at Hugging Face. He has built machine learning applications for startups and enterprises in the domains of NLP, topological data analysis, and time series. Lewis has a PhD in theoretical physics and has held research positions in Australia, the USA, and Switzerland. His current work focuses on developing tools for the NLP community and teaching people to use them effectively.

    See more on the author's page
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