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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Question-answering is mostly presented as an NLP exercise involving a transformer and a dataset that contains the ready-to-ask questions and provides the answers to those questions. The transformer is trained to answer the questions asked in this closed environment.

However, in more complex situations, reliable transformer model implementations require customized methods.

Transformers and methods

A perfect and efficient universal transformer model for question-answering or any other NLP task does not exist. The best model for a project is the one that produces the best outputs for a specific dataset and task.

Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models, showed that the Pattern-Exploiting Training (PET) method applied to a small ALBERT model exceeded the performance of the much larger GPT-3 model.

The method outperforms models in many cases. A suitable method with an average model often will produce more efficient results than a flawed...