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

Choosing a model and an ecosystem

We thought that testing transformer models by downloading them would require machine and human resources. Also, you might have thought that if a platform doesn’t have an online sandbox by this time, it will be a risk to go further because of the work to test a few examples.

However, sites such as Hugging Face download pretrained models automatically in real time, as we will see in The Reformer and DeBERTa sections! So, what should we do? Thanks to that, we can run Hugging Face models in Google Colab without installing anything on the machine ourselves. We can also test Hugging Face models online.

The idea is to analyze without having anything to “install.” “Nothing to Install” in 2022 can mean:

  • Running a transformer task online
  • Running a transformer on a preinstalled Google Colaboratory VM that seamlessly downloads a pretrained model for a task, which we can run in a few lines
  • Running...