English | 2022 | ISBN: ‎ 1803247339 | 565 pages | True PDF EPUB | 27.67 MB

OpenAI's GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and code writing and assistance with Codex and GitHub Copilot

Key Features

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

Perform root cause analysis on hard NLP problems

Book Description

Transformers are...well...transfog 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 Codex.

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

Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E

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 sennt analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3

Measure the productivity of key transformers to define their scope, potential, and limits in production

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 and author, Denis Rothman. So, he'll be there to guide you on your transformers journey!

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 Ees

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

(N.B. Please use the Look Inside option to see further chapters)