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

In this chapter, we found that question-answering isn’t as easy as it seems. Implementing a transformer model only takes a few minutes. However, getting it to work can take a few hours or several months!

We first asked the default transformer in the Hugging Face pipeline to answer some simple questions. DistilBERT, the default transformer, answered the simple questions quite well. However, we chose easy questions. In real life, users ask all kinds of questions. The transformer can get confused and produce erroneous output.

We then decided to continue to ask random questions and get random answers, or we could begin to design the blueprint of a question generator, which is a more productive solution.

We started by using NER to find useful content. We designed a function that could automatically create questions based on NER output. The quality was promising but required more work.

We tried an ELECTRA model that did not produce the results we expected...