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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

Matching Tokenizers and Datasets

When studying transformer models, we tend to focus on the models’ architecture and the datasets provided to train them. We have explored the original Transformer, fine-tuned a BERT-like model, trained a RoBERTa model, explored a GPT-3 model, trained a GPT-2 model, implemented a T5 model, and more. We have also gone through the main benchmark tasks and datasets.

We trained a RoBERTa tokenizer and used tokenizers to encode data. However, we did not explore the limits of tokenizers to evaluate how they fit the models we build. AI is data-driven. Raffel et al. (2019), like all the authors cited in this book, spent time preparing datasets for transformer models.

In this chapter, we will go through some of the limits of tokenizers that hinder the quality of downstream transformer tasks. Do not take pretrained tokenizers at face value. You might have a specific dictionary of words you use (advanced medical language, for example) with words...

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Transformers for Natural Language Processing
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