Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Transformers for Natural Language Processing - Second Edition

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
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman

Table of Contents (25) Chapters

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

Google Colab Free with a CPU

It is nearly impossible to fine-tune or train a transformer model with millions or billions of parameters on a CPU. CPUs are mostly sequential. Transformer models are designed for parallel processing.

In the Runtime menu and Change Runtime Type submenu, you can select a hardware accelerator: None (CPU), GPU, or TPU.

This test was run with None (CPU), as shown in Figure II.2:

Graphical user interface, text, application, chat or text message  Description automatically generated

Figure II.2: Selecting a hardware accelerator

When the notebook reaches the training loop, it slows down right from the start:

Figure II.3: Training loop

After 15 minutes, nothing has really happened.

CPUs are not designed for parallel processing. Transformer models are designed for parallel processing, so part from toy models, they require GPUs.

Google Colab Free with a GPU

Let’s go back to the notebook settings to select a GPU.

Une image contenant texte  Description générée automatiquement

Figure II.4 Selecting a GPU

At the time of writing, I tested Google Colab, and an NVIDIA...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}