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

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Length 602 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Denis Rothman

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Table of Contents (25) Chapters

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

Chapter 17, The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4

GPT-4 is sentient. (True/False)
False. GPT-4 is a mathematical algorithm. It does not need to be sentient to learn statistical patterns to do a wide variety of tasks.
ChatGPT can replace a human expert. (True/False)
False. ChatGPT can produce results based on its datasets. However, it cannot make subject matter expert (SME) decisions.
GPT-4 can generate source code for any task? (True/False)
False. GPT-4 can generate source code for many tasks. However, for complex problems, human intervention is required.
Advanced prompt engineering is intuitive. (True/False)
False. Advanced prompt engineering has become a skill that is based on in-depth knowledge of transformers. Advanced prompt engineering involves building knowledge bases, multiple types of objects for the completion of APIs, and understanding the many models available.
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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

Table of Contents (25) Chapters

Chapter 17, The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4

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