rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Natural language processing with transformers
    sub title: building language applications with Hugging Face
  name:
    name part: Tunstall, Lewis
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Werra, Leandro von
    role:
      role term: author
  name:
    name part: Wolf, Thomas
    name part: (Of HuggingFace)
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: cau
    date issued: 2023
    copyright date: 2022
    edition: Revised edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: 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.
  note: Lewis Tunstall, Leandro von Werra & Thomas Wolf.
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Machine learning
  subject:
    topic: Cloud computing
  subject:
    topic: Cloud computing
  subject:
    topic: Machine learning
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Python (Computer program language)
  classification: QA76.9.N38 T86 2023
  classification: 006.3/5
  related item:
    title info:
      title: Natural Language Processing with Transformers, Revised Edition
    name:
      name part: Tunstall, Lewis
    origin info:
      publisher: Sebastopol : O'Reilly Media, Incorporated,c2022
  identifier: 1098136764
  identifier: 9781098136765
  identifier: 9781098136758
  identifier: 1098136756
  identifier: 9781098136796
  identifier: 9781098136789 O'Reilly Media
  identifier: 0BD50887-2146-4BA5-A6A1-00378AF435E8 OverDrive, Inc.
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 220529
    record change date: 20260130164706.8
    record identifier: in00004671373
    language of cataloging:
      language term: eng