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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
Languages
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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? FREE CHAPTER 2. Getting Started with the Architecture of the Transformer Model 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

Defining machine translation

Vaswani et al. (2017) tackled one of the most difficult NLP problems when designing the Transformer. The human baseline for machine translation seems out of reach for us human-machine intelligence designers. This did not stop Vaswani et al. (2017) from publishing the Transformer’s architecture and achieving state-of-the-art BLEU results.

In this section, we will define machine translation. Machine translation is the process of reproducing human translation by machine transductions and outputs:

Figure 6.1: Machine translation process

The general idea in Figure 6.1 is for the machine to do the following in a few steps:

  • Choose a sentence to translate
  • Learn how words relate to each other with hundreds of millions of parameters
  • Learn the many ways in which words refer to each other
  • Use machine transduction to transfer the learned parameters to new sequences
  • Choose a candidate translation for a word...
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