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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

From NLP to Task-Agnostic Transformer Models

Up to now, we have examined variations of the original Transformer model with encoder and decoder layers, and we explored other models with encoder-only or decoder-only stacks of layers. Also, the size of the layers and parameters has increased. However, the fundamental architecture of the transformer retains its original structure with identical layers and the parallelization of the computing of the attention heads.

In this chapter, we will explore innovative transformer models that respect the basic structure of the original Transformer but make some significant changes. Scores of transformer models will appear, like the many possibilities a box of LEGO© pieces gives. You can assemble those pieces in hundreds of ways! Transformer model sublayers and layers are the LEGO© pieces of advanced AI.

We will begin by asking which transformer model to choose among the many offers and the ecosystem we will implement them in.

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Transformers for Natural Language Processing
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