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

Getting started with the GPT-4 API

In this section, we will get started with the GPT-4 API using ChatGPT Plus GPT-4 as a copilot. You will see how a cutting-edge developer can reduce the time to market with GPT-4 as a copilot.

Most of this section was written in the notebook by the author but also with the support of GPT-4. GPT-4’s comments are preceded by GPT-4:, which is a standard ethical procedure.

First, open Getting_Started_with_GPT_4.ipynb.

This section follows the structure and comments of the notebook. Working at this speed and comfort is an exhilarating experience.

1. GPT-4 helps to explain how to write source code

Give GPT-4 instructions with well-crafted prompts to steer the model.

It will return the source code. This notebook was designed with Python but you can try other languages.

In the top-right corner of the source code frame, click on Copy code:

Figure 17.4: Copy code option

Paste the code in a code cell in your notebook...

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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

Getting started with the GPT-4 API

1. GPT-4 helps to explain how to write source code

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Authors (1)