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Transformers for Natural Language Processing

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

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (25) Chapters Close

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

Fine-Tuning BERT Models

In Chapter 2, Getting Started with the Architecture of the Transformer Model, we defined the building blocks of the architecture of the original Transformer. Think of the original Transformer as a model built with LEGO® bricks. The construction set contains bricks such as encoders, decoders, embedding layers, positional encoding methods, multi-head attention layers, masked multi-head attention layers, post-layer normalization, feed-forward sub-layers, and linear output layers.

The bricks come in various sizes and forms. You can spend hours building all sorts of models using the same building kit! Some constructions will only require some of the bricks. Other constructions will add a new piece, just like when we obtain additional bricks for a model built using LEGO® components.

BERT added a new piece to the Transformer building kit: a bidirectional multi-head attention sub-layer. When we humans have problems understanding a sentence, we do...

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