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 8, Applying Transformers to Legal and Financial Documents for AI Text Summarization

  1. T5 models only have encoder stacks like BERT models. (True/False)

    False.

  1. T5 models have both encoder and decoder stacks. (True/False)

    True.

  1. T5 models use relative positional encoding, not absolute positional encoding. (True/False)

    True.

  1. Text-to-text models are only designed for summarization. (True/False)

    False.

  1. Text-to-text models apply a prefix to the input sequence that determines the NLP task. (True/False)

    True.

  1. T5 models require specific hyperparameters for each task. (True/False)

    False.

  1. One of the advantages of text-to-text models is that they use the same hyperparameters for all NLP tasks. (True/False)

    True.

  1. T5 transformers do not contain a feedforward network. (True/False)

    False.

  1. Hugging Face is a framework...