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

View More author details

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

Copilots

Welcome to the world of AI-driven development copilots powered by OpenAI and available in Visual Studio.

GitHub Copilot

Let’s begin with GitHub Copilot:

https://github.com/github/copilot-docs

In this section, we will use GitHub Copilot with PyCharm (JetBrains):

https://github.com/github/copilot-docs/tree/main/docs/jetbrains

Follow the instructions in the documentation to install JetBrains and activate OpenAI GitHub Copilot in PyCharm.

Working withGitHub Copilot is a four-step process (see Figure 16.7):

OpenAI Codex is trained on public code and text on the internet.
The trained model is plugged into the GitHub Copilot service.
The GitHub service manages back-and-forth flows between code we write in an editor (in this case PyCharm) and OpenAI Codex. The GitHub Service Manager makes suggestions and then sends the interactions back for improvement.
The code editor is our development workspace.

A picture containing diagram Description automatically generated

Figure...

The rest of the chapter is locked

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

Copilots

GitHub Copilot

Unlock this book and the full library FREE for 7 days

Authors (1)