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Transformers for Natural Language Processing

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
3.8 (28)
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Transformers for Natural Language Processing

Transformers for Natural Language Processing

3.8 (28)
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)
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18
Other Books You May Enjoy
19
Index
1
Appendix I — Terminology of Transformer Models

Interpreting Black Box Transformer Models

Million- to billion-parameter transformer models seem like huge black boxes that nobody can interpret. As a result, many developers and users have sometimes been discouraged when dealing with these mind-blowing models. However, recent research has begun to solve the problem with innovative, cutting-edge tools.

It is beyond the scope of this book to describe all of the explainable AI methods and algorithms. So instead, this chapter will focus on ready-to-use visual interfaces that provide insights for transformer model developers and users.

The chapter begins by installing and running BertViz by Jesse Vig. Jesse did quite an excellent job of building a visual interface that shows the activity in the attention heads of a BERT transformer model. BertViz interacts with the BERT models and provides a well-designed interactive interface.

We will continue to focus on visualizing the activity of transformer models with the Language Interpretability...

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Transformers for Natural Language Processing
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