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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

The Rise of Suprahuman Transformers with GPT-3 Engines

In 2020, Brown et al. (2020) described the training of an OpenAI GPT-3 model containing 175 billion parameters that learned using huge datasets such as the 400 billion byte-pair-encoded tokens extracted from Common Crawl data. OpenAI ran the training on a Microsoft Azure supercomputer with 285,00 CPUs and 10,000 GPUs.

The machine intelligence of OpenAI’s GPT-3 engines and their supercomputer led Brown et al. (2020) to zero-shot experiments. The idea was to use a trained model for downstream tasks without further training the parameters. The goal would be for a trained model to go directly into multi-task production with an API that could even perform tasks it wasn’t trained for.

The era of suprahuman cloud AI engines was born. OpenAI’s API requires no high-level software skills or AI knowledge. You might wonder why I used the term “suprahuman.” You will discover that a GPT-3 engine can...

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Transformers for Natural Language Processing
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