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

Questions

  1. Machine translation has now exceeded human baselines. (True/False)
  2. Machine translation requires large datasets. (True/False)
  3. There is no need to compare transformer models using the same datasets. (True/False)
  4. BLEU is the French word for blue and is the acronym of an NLP metric (True/False)
  5. Smoothing techniques enhance BERT. (True/False)
  6. German-English is the same as English-German for machine translation. (True/False)
  7. The original Transformer multi-head attention sub-layer has 2 heads. (True/False)
  8. The original Transformer encoder has 6 layers. (True/False)
  9. The original Transformer encoder has 6 layers but only 2 decoder layers. (True/False)
  10. You can train transformers without decoders. (True/False)