Book Image

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
5 (1)
Book Image

Transformers for Natural Language Processing - Second Edition

5 (1)
By: Denis Rothman

Overview of this book

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.
Table of Contents (25 chapters)
18
Other Books You May Enjoy
19
Index
Appendix I — Terminology of Transformer Models

The architecture of OpenAI GPT transformer models

Transformers went from training, to fine-tuning, and finally to zero-shot models in less than three years between the end of 2017 and the first part of 2020. A zero-shot GPT-3 transformer model requires no fine-tuning. The trained model parameters are not updated for downstream multi-tasks, which opens a new era for NLP/NLU tasks.

In this section, we will first learn about the motivation of the OpenAI team that designed GPT models. We will begin by going through the fine-tuning of zero-shot models. Then we will see how to condition a transformer model to generate mind-blowing text completion. Finally, we will explore the architecture of GPT models.

We will first go through the creation process of the OpenAI team.

The rise of billion-parameter transformer models

The speed at which transformers went from small models trained for NLP tasks to models that require little to no fine-tuning is staggering.

Vaswani et al...