Natural Language Processing with Transformers

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 machine learning engineer, this practical book shows you how to train and scale these large models using HuggingFace 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 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

Lewis Tunstall is a data scientist in Switzerland, focused on building machine learning powered applications for startups and enterprises in the domains of natural language processing and time series. A former theoretical physicist, he has over 10 years experience translating complex subject matter to lay audiences and has taught machine learning to university students at both ...

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