Natural Language Processing with Transformers, Revised Edition

By: Lewis Tunstall, Leandro von Werra, Thomas Wolf

Write A Review

eText | 26 May 2022 | Edition Number 1

At a Glance

Format
ePUB

eText

$64.89

or 4 interest-free payments of $16.22 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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