Lewis Tunstall - Natural Language Processing with Transformers: Building Language Applications with Hugging Face

by Lewis Tunstall , Leandro von Werra , and Thomas Wolf

Copyright 2022 Lewis Tunstall, Leandro von Werra, and Thomas Wolf. All rights reserved.

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A miracle is taking place as you read these lines: the squiggles on this page are transforming into words and concepts and emotions as they navigate their way through your cortex. My thoughts from November 2021 have now successfully invaded your brain. If they manage to catch your attention and survive long enough in this harsh and highly competitive environment, they may have a chance to reproduce again as you share these thoughts with others. Thanks to language, thoughts have become airborne and highly contagious brain germsand no vaccine is coming.

Luckily, most brain germs are harmless, and a few are wonderfully useful. In fact, humanitys brain germs constitute two of our most precious treasures: knowledge and culture. Much as we cant digest properly without healthy gut bacteria, we cannot think properly without healthy brain germs. Most of your thoughts are not actually yours: they arose and grew and evolved in many other brains before they infected you. So if we want to build intelligent machines, we will need to find a way to infect them too.

The good news is that another miracle has been unfolding over the last few years: several breakthroughs in deep learning have given birth to powerful language models. Since you are reading this book, you have probably seen some astonishing demos of these language models, such as GPT-3, which given a short prompt such as a frog meets a crocodile can write a whole story. Although its not quite Shakespeare yet, its sometimes hard to believe that these texts were written by an artificial neural network. In fact, GitHubs Copilot system is helping me write these lines: youll never know how much I really wrote.

The revolution goes far beyond text generation. It encompasses the whole realm of natural language processing (NLP), from text classification to summarization, translation, question answering, chatbots, natural language understanding (NLU), and more. Wherever theres language, speech or text, theres an application for NLP. You can already ask your phone for tomorrows weather, or chat with a virtual help desk assistant to troubleshoot a problem, or get meaningful results from search engines that seem to truly understand your query. But the technology is so new that the best is probably yet to come.

Like most advances in science, this recent revolution in NLP rests upon the hard work of hundreds of unsung heroes. But three key ingredients of its success do stand out:

• The transformer is a neural network architecture proposed in 2017 in a groundbreaking paper called Attention Is All You Need, published by a team of Google researchers. In just a few years it swept across the field, crushing previous architectures that were typically based on recurrent neural networks (RNNs). The Transformer architecture is excellent at capturing patterns in long sequences of data and dealing with huge datasetsso much so that its use is now extending well beyond NLP, for example to image processing tasks.