Natural Language Processing with Transformers: Building Language Applications with Hugging Face - Softcover
Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Lewis Tunstall is a machine learning engineer at Hugging Face. 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, where he primarily works on code generation models and community outreach. Thomas Wolf is chief science officer at and cofounder of Hugging Face. His team is on a mission to catalyze and democratize NLP research.
"About this title" may belong to another edition of this title.
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