Overview

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, Lewis Tunstall, Leandro von Werra, and Thomas Wolf 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 ● Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

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Lewis Tunstall is a data scientist at Swisscom, focused on building machine learning powered applications in the domains of natural language processing and time series. A former theoretical physicist, he has over ten years' experience translating complex subject matter to lay audiences and has taught machine learning to university students at both the graduate and undergraduate levels. Leandro von Werra is a data scientist at Swiss Mobiliar, where he leads the company's natural language processing efforts to streamline and simplify processes for customers and employees. He has experience working across the whole machine learning stack, and is the creator of a popular Python library that combines Transformers with reinforcement learning. He also teaches data science and visualization at the Bern University of Applied Sciences. Thomas Wolf is chief science officer and cofounder of HuggingFace. His team is on a mission to catalyze and democratize NLP research. Prior to HuggingFace, Thomas gained a PhD in physics, and later a law degree. He worked as a physics researcher and a European patent attorney. Tom Beyer is a character actor who has appeared in over 100 TV shows, films, and commercials; has performed in innumerable plays and musicals; and has narrated many audiobooks. Grown in New York, fermented in Seattle, and aged in Los Angeles, his passions include Shakespeare, reading, intense physical exercise, and animal rescue. He has won awards for his stage work as both an actor and a director, and has adapted classical literature for the theater. He believes strongly in civic engagement, and volunteers for multiple organizations.

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