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? 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

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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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

Summary

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Authors (1)