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Transformers for natural language processing : build, train, and fine-tuning deep neural network architectures for NLP with Python, Hugging face, and openAI's GPT-3, ChatGPT, and GPT4 / Denis Rothman ; foreword by Antonio Gulli

By: Contributor(s): Material type: TextTextSeries: Expert insightPublisher: [Birmingham, United Kingdom] : Packt Publishing, [2022]Edition: Second editionDescription: xxiii, 565 pages : illustrations ; 23 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): Genre/Form: DDC classification:
  • 006.3 ROT 2022 23
LOC classification:
  • Q336
Summary: Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets
List(s) this item appears in: New Book 2024
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Holdings
Item type Current library Home library Collection Shelving location Call number Status Date due Barcode Item holds
Open Collection Open Collection FIRST CITY UNIVERSITY COLLEGE FIRST CITY UNIVERSITY COLLEGE Open Collection FCUC Library 006.3 ROT 2022 (Browse shelf(Opens below)) Available 00025125
Total holds: 0

Includes index

Available to OhioLINK libraries

Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets