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

Computer vision

This book is about NLP, not computer vision. However, in the previous section, we implemented general purpose sequences that can be applied to many domains. Computer vision is one of them.

The title of the article by Dosovitskiy et al. (2021) says it all: An image is worth 16x16 words: Transformers for Image Recognition at Scale. The authors processed an image as sequences. The results proved their point.

Google has made vision transformers available in a Colaboratory notebook. Open Vision_Transformer_MLP_Mixer.ipynb in the Chapter16 directory of this book’s GitHub repository.

Open Vision_Transformer_MLP_Mixer.ipynb contains a transformer computer vision model in JAX(). JAX combines Autograd and XLA. JAX can differentiate Python and NumPy functions. JAX speeds up Python and NumPy by using compilation techniques and parallelization.

The notebook is self-explanatory. You can explore it to see how it works. However, bear in mind that when Industry...