Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
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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, trained a GPT-2 model, and implemented a T5 model. 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. Artificial intelligence is data-driven. Raffel et al. (2019), like all of 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 are using (advanced medical language, for example) with words that...