Natural Language Processing with Transformers, Rev eBook

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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 -now revised in full color- 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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Additional ISBNs

Natural Language Processing with Transformers, Revised Edition 1st Edition is written by Lewis Tunstall; Leandro von Werra; Thomas Wolf and published by O’Reilly Media. The Digital and eTextbook ISBNs for Natural Language Processing with Transformers, Revised Edition are 9781098136758, 1098136756 and the print ISBNs are 9781098136796, 1098136799. Additional ISBNs for this eTextbook include 9781098136765.