Natural Language Processing with Transformers, Revised Edition
$65.99
168 in stock
Refresh Stock LevelInformation
Shipping
We currently offer free shipping on all orders over $100. Standard media mail shipping is $7.50 plus $1 for each additional book. Electronics are $35 shipping on all items.
Books
We get our books from a national distributor and although we strive to present up to date stock counts, stock constantly fluctuates. We perform a stock check when you add your book to the cart to ensure that it is available for shipping from the distributor. You can also check stock status by clicking the refresh stock link on the product page for the most up to date stock at the distributor. If an item is on backorder, you may place an order and we will update you on the estimated ship date as soon as we can confirm with the distributor.
Return & exchange
If you are not satisfied with your purchase you can return it to us within 14 days for an exchange or refund. More info.
Assistance
Can’t find what you’re looking for? We have access to over 13 million titles, reach out and see if we can help!
Contact us on (575) 322-6867, or email us at business@rabsbooks.com.
| Weight | 1.43 lbs |
|---|---|
| Dimensions | 9.19 × 7 × 0.84 in |
Description
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
O’Reilly Media
Reviews
There are no reviews yet.