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

Transduction and the inductive inheritance of transformers

The emergence of Automated Machine Learning (AutoML), meaning APIs in automated cloud AI platforms, has deeply changed the job description of every AI specialist. Google Vertex, for example, boasts a reduction of 80% of the development required to implement ML. This suggests that anybody can implement ML with ready-to-use systems. Does that mean an 80% reduction of the workforce of developers? I don’t think so. I see an Industry 4.0 AI specialist assemble AI with added value to a project.

Industry 4.0. NLP AI specialists invest less in source code and more in knowledge to become the AI guru of a team.

Transformers possess the unique ability to apply their knowledge to tasks they did not learn. A BERT transformer, for example, acquires language through sequence-to-sequence and masked language modeling. The BERT transformer can then be fine-tuned to perform downstream tasks that it did not learn from...

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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

Transduction and the inductive inheritance of transformers

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