You're reading from Transformers for Natural Language Processing - Second Edition

Product type Book

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Pages 602 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Denis Rothman

Preface

1. What are Transformers?

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

Questions

Reformer transformer models don’t contain encoders. (True/False)
Reformer transformer models don’t contain decoders. (True/False)
The inputs are stored layer by layer in Reformer models. (True/False)
DeBERTa transformer models disentangle content and positions. (True/False)
It is necessary to test the hundreds of pretrained transformer models before choosing one for a project. (True/False)
The latest transformer model is always the best. (True/False)
It is better to have one transformer model per NLP task than one multi-task transformer model. (True/False)
A transformer model always needs to be fine-tuned. (True/False)
OpenAI GPT-3 engines can perform a wide range of NLP tasks without fine-tuning. (True/False)
It is always better to implement an AI algorithm on a local server. (True/False)