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

Some Pragmatic I4.0 thinking before we leave

The sentiment analysis with Hugging Face transformers contained a sentence that came out as “neutral.”

But is that true?

Labeling this sentence “neutral” bothered me. I was curious to see if OpenAI GPT-3 could do better. After all, GPT-3 is a foundation model that can theoretically do many things it wasn’t trained for.

I examined the sentence again:

Though the customer seemed unhappy, she was, in fact, satisfied but thinking of something else at the time, which gave a false impression.

When I read the sentence closely, I could see that the customer is she. When I looked deeper, I understood that she is in fact satisfied. I decided not to try models blindly until I reached one that works. Trying one model after the other is not productive.

I needed to get to the root of the problem using logic and experimentation. I didn’t want to rely on an algorithm that would find the...