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

by Denis Rothman, Antonio Gulli (Foreword by)

Overview

Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?

Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.

You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.

If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.

The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).

You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4.

By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.

Product Details

ISBN-13:	9781803243481
Publisher:	Packt Publishing
Publication date:	03/25/2022
Sold by:	Barnes & Noble
Format:	eBook
Pages:	602
File size:	20 MB
Note:	This product may take a few minutes to download.

About the Author

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.

Table of Contents

Table of Contents

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. Appendix I — Terminology of Transformer Models
19. Appendix II — Hardware Constraints for Transformer Models
20. Appendix III — Generic Text Completion with GPT-2
21. Appendix IV — Custom Text Completion with GPT-2
22. Appendix V — Answers to the Questions