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

Chapter 14, Interpreting Black Box Transformer Models

  1. BERTViz only shows the output of the last layer of the BERT model. (True/False)

    False. BERTViz displays the outputs of all the layers.

  1. BERTViz shows the attention heads of each layer of a BERT model. (True/False)

    True.

  1. BERTViz shows how the tokens relate to each other. (True/False)

    True.

  1. LIT shows the inner workings of the attention heads like BERTViz. (True/False)

    False. However, LIT makes non-probing predictions.

  1. Probing is a way for an algorithm to predict language representations. (True/False)

    True.

  1. NER is a probing task. (True/False)

    True.

  1. PCA and UMAP are non-probing tasks. (True/False)

    True.

  1. LIME is model-agnostic. (True/False)

    True.

  1. Transformers deepen the relationships of the tokens layer by layer. (True/False)

    True.

    ...