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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (25) Chapters Close

Preface 1. What are Transformers? 2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER 3. Fine-Tuning BERT Models 4. Pretraining a RoBERTa Model from Scratch 5. Downstream NLP Tasks with Transformers 6. Machine Translation with the Transformer 7. The Rise of Suprahuman Transformers with GPT-3 Engines 8. Applying Transformers to Legal and Financial Documents for AI Text Summarization 9. Matching Tokenizers and Datasets 10. Semantic Role Labeling with BERT-Based Transformers 11. Let Your Data Do the Talking: Story, Questions, and Answers 12. Detecting Customer Emotions to Make Predictions 13. Analyzing Fake News with Transformers 14. Interpreting Black Box Transformer Models 15. From NLP to Task-Agnostic Transformer Models 16. The Emergence of Transformer-Driven Copilots 17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4 18. Other Books You May Enjoy
19. Index
Appendix I — Terminology of Transformer Models 1. Appendix II — Hardware Constraints for Transformer Models 2. Appendix III — Generic Text Completion with GPT-2 3. Appendix IV — Custom Text Completion with GPT-2 4. Appendix V — Answers to the Questions

The rise of the Transformer: Attention is All You Need

In December 2017, Vaswani et al. (2017) published their seminal paper, Attention is All You Need. They performed their work at Google Research and Google Brain. I will refer to the model described in Attention is All You Need as the “original Transformer model” throughout this chapter and book.

Appendix I, Terminology of Transformer Models, can help the transition from the classical usage of deep learning words to transformer vocabulary. Appendix I summarizes some of the changes to the classical AI definition of neural network models.

In this section, we will look at the structure of the Transformer model they built. In the following sections, we will explore what is inside each component of the model.

The original Transformer model is a stack of 6 layers. The output of layer l is the input of layer l+1 until the final prediction is reached. There is a 6-layer encoder stack on the left and...

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Transformers for Natural Language Processing - Second Edition
Published in: Mar 2022
Publisher: Packt
ISBN-13: 9781803247335
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