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Transformers for Natural Language Processing - Second Edition

You're reading from  Transformers for Natural Language Processing - Second Edition

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Pages 602 pages
Edition 2nd Edition
Languages
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman

Table of Contents (25) Chapters

Preface 1. What are Transformers? 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

Summary

In this chapter, we built on the knowledge and expertise you acquired in the previous chapters to tackle OpenAI’s state-of-the-art transformer models.

The knowledge you now possess enables you to make an incremental step forward from the GPT-3 model you already discovered in Chapter 7, The Rise of Suprahuman Transformers with GPT-3 Engines. For those who are beginning to learn transformers now, the path will be quite long.

We first jump-started ChatGPT using the same approach as in Chapter 7. The new step was to implement a conversational AI prompt. You saw how ChatGPT Plus can generate a k-means clustering classification program, plot the outputs, and provide explanations.

Getting started with GPT-4 was an incremental step forward in implementing a powerful general-purpose transformer model.

Model exploration took you into the world of 50+ transformer models, including davinci, GPT-3.5-turbo, and GPT-4.

Explainable AI by ChatGPT opened the OpenAI...

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