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  • Book Overview & Buying Transformers for Natural Language Processing
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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
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Stack

A stack contains identically sized layers that differ from classical deep learning models, as shown in Figure I.1. A stack runs from bottom to top. A stack can be an encoder or a decoder.

Figure I.1: Layers form a stack

Transformer stacks learn and see more as they rise in the stacks. Each layer transmits what it learned to the next layer just as our memory does.

Imagine that a stack is the Empire State Building in New York City. At the bottom, you cannot see much. But you will see more and farther as you ascend throught the offices on higher floors and look out the windows. Finally, at the top, you have a fantastic view of Manhattan!

Visually different images
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Transformers for Natural Language Processing
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