Transformers for Natural Language Processing - Second Edition: 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

Overview

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

• Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models
• Pretrain a BERT-based model from scratch using Hugging Face
• Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data

Book Description

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.

What you will learn

• Discover new techniques to investigate complex language problems
• Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers
• Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3
• Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E
• Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4

Who this book is for

If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!

Product Details

ISBN-13:	9781803247335
Publisher:	Packt Publishing
Publication date:	03/25/2022
Edition description:	2nd ed.
Pages:	602
Product dimensions:	7.50(w) x 9.25(h) x 1.22(d)

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