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
2nd Edition
Published on 25. March 2022
Book
Paperback/Softback
602 pages
978-1-80324-733-5 (ISBN)
Description
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 DescriptionTransformers 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 forIf 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!
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 DescriptionTransformers 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 forIf 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!
More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 33 mm
Weight
1108 gr
ISBN-13
978-1-80324-733-5 (9781803247335)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Denis Rothman
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
E-Book
09/2024
2nd Edition
Packt Publishing
from
€64.79
Available for download
Person
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.
Content
Table of Contents
What are Transformers?
Getting Started with the Architecture of the Transformer Model
Fine-Tuning BERT Models
Pretraining a RoBERTa Model from Scratch
Downstream NLP Tasks with Transformers
Machine Translation with the Transformer
The Rise of Suprahuman Transformers with GPT-3 Engines
Applying Transformers to Legal and Financial Documents for AI Text Summarization
Matching Tokenizers and Datasets
Semantic Role Labeling with BERT-Based Transformers
Let Your Data Do the Talking: Story, Questions, and Answers
Detecting Customer Emotions to Make Predictions
Analyzing Fake News with Transformers
Interpreting Black Box Transformer Models
From NLP to Task-Agnostic Transformer Models
The Emergence of Transformer-Driven Copilots
The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4
Appendix I - Terminology of Transformer Models
Appendix II - Hardware Constraints for Transformer Models
Appendix III - Generic Text Completion with GPT-2
Appendix IV - Custom Text Completion with GPT-2
Appendix V - Answers to the Questions
What are Transformers?
Getting Started with the Architecture of the Transformer Model
Fine-Tuning BERT Models
Pretraining a RoBERTa Model from Scratch
Downstream NLP Tasks with Transformers
Machine Translation with the Transformer
The Rise of Suprahuman Transformers with GPT-3 Engines
Applying Transformers to Legal and Financial Documents for AI Text Summarization
Matching Tokenizers and Datasets
Semantic Role Labeling with BERT-Based Transformers
Let Your Data Do the Talking: Story, Questions, and Answers
Detecting Customer Emotions to Make Predictions
Analyzing Fake News with Transformers
Interpreting Black Box Transformer Models
From NLP to Task-Agnostic Transformer Models
The Emergence of Transformer-Driven Copilots
The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4
Appendix I - Terminology of Transformer Models
Appendix II - Hardware Constraints for Transformer Models
Appendix III - Generic Text Completion with GPT-2
Appendix IV - Custom Text Completion with GPT-2
Appendix V - Answers to the Questions