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 DALL-E 2 API

DALL-E’s text-to-image functionality has dramatically improved in a short time. Transformers are task-agnostic and can perform a variety of tasks. Transformers are now also multimodal (audio, image, other signals).

We previously went through DALL-E’s architecture in the DALL-E section of Chapter 15, From NLP to Task-Agnostic Transformer Models.

You can try DALL-E 2 online: https://openai.com/product/dall-e-2.

However, this section goes further using the second-generation API of the DALL-E 2 API so that we can write a program. The API allows us to create, modify, and generate variations of an image.

Open Getting_Started_with_the_DALL_E_2_API.ipynb.

You can implement the DALL-E 2 API as you see fit for your project.

The notebook presents one way of running the DALL-E 2 API.

You can run it cell by cell to grasp the functionality provided or run the whole notebook for one scenario.

This section is divided...

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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 DALL-E 2 API

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Authors (1)