Transformers for Natural Language Processing – Second Edition

Transformers for Natural Language Processing, 2nd Edition

eBook Details:

  • Paperback: 564 pages
  • Publisher: WOW! eBook; 2nd edition (March 25, 2022)
  • Language:¬†English
  • ISBN-10: 1803247339
  • ISBN-13: 978-1803247335

eBook Description:

Transformers for Natural Language Processing, 2nd Edition: Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP

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.

  • Discover new ways of performing NLP techniques with the latest pretrained transformers
  • Grasp the workings of the original Transformer, GPT-3, BERT, T5, DeBERTa, and Reformer
  • Find out how ViT and CLIP label images (including blurry ones!) and reconstruct images using DALL-E
  • Carry out sentiment analysis, text summarization, casual language analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3
  • Measure the productivity of key transformers to define their scope, potential, and limits in production

By the end of this Transformers for Natural Language Processing, Second Edition book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets.

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1 Response

  1. May 12, 2022

    […] Natural Language Processing with Flair: Learn how to solve practical NLP problems with the Flair Python framework, train sequence labeling models, work with text classifiers and word embeddings, and much more through hands-on practical exercises […]

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