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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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