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

Summary

This chapter analyzed the difference between the human language representation process and the way machine intelligence performs transduction. We saw that transformers must rely on the outputs of our incredibly complex thought processes expressed in written language. Language remains the most precise way to express a massive amount of information. The machine has no senses and must convert speech to text to extract meaning from raw datasets.

We then explored how to measure the performance of multi-task transformers. Transformers’ ability to obtain top-ranking results for downstream tasks is unique in NLP history. We went through the tough SuperGLUE tasks that brought transformers up to the top ranks of the GLUE and SuperGLUE leaderboards.

BoolQ, CB, WiC, and the many other tasks we covered are by no means easy to process, even for humans. We went through an example of several downstream tasks that show the difficulty transformer models face in proving their...