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
