Natural Language Processing with Transformers: Revised Edition (Full Colour Edition)

Building Language Applications with Hugging Face

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9789355420329
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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
    Books
    Author
    Lewis Tunstall
    Binding
    Paperback
    Condition Type
    New
    Country Origin
    India
    Edition
    First
    Gift Wrap
    N
    Lead time to ship in days (if not in stock)
    ships in 12-15 days
    Leadtime to ship in days (default)
    ships in 1-2 days
    Pages
    408
    Publication Date
    16/06/2022
    Publisher
    Shroff/O'Reilly
    Year
    2022
    Dimensions
    Dimensions (W x H x D)
    7 x 9 x 0.75 inch

    Table of Contents (9789355420329_toc.pdf, 179 Kb) [Download]

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