Writing a book on NLP is a bit like solving a complex data science project
A series of interviews highlighting the incredible work of writers in the space of data science and their path of writing.
"In fiction, the language and the senses it evokes are important, whereas in technical writing, the content, and the information it conveys, are important." ― Krista Van Laan, The Insider's Guide to Technical Writing
Last edited on Feb 6, 2023
Being a writer myself, I have a keen interest in uncovering the narratives behind the books we read, especially in the machine learning realm. These writers possess an uncanny ability to translate the complexities of AI into words that are both informative and interesting is truly remarkable. It is my goal, through a series of interviews, to bring their stories to the forefront and shed light on the story of some of the well-known authors in the field of Artificial Intelligence.
Meet the Author: Lewis Tunstall
Lewis Tunstall is an accomplished machine learning engineer currently working at Hugging Face. He has extensive experience in building machine learning applications for startups and enterprises, with a focus on the areas of NLP, topological data analysis, and time series. With a PhD in theoretical physics, Lewis has had the opportunity to hold research positions in various countries, including Australia, the USA, and Switzerland. His current work focuses on developing innovative tools for the NLP community and empowering individuals with the knowledge and skills to use them effectively.
Lewis is the co-author of the book -" Natural Language Processing with Transformers" along with Leandro von Werra and Thomas Wolf. The book is a comprehensive guide to the latest advancements in the field of NLP and is a great resource for anyone looking to gain a deeper understanding of NLP and how it can be applied to real-world problems.
Natural Language Processing with Transformers, Revised Edition
Q: How did the idea of this book originate?
Lewis: Although we began the book in 2020, its origin story really began in 2019 when Leandro and I first started working with Transformer models. At the time, Jay Alammar's amazing blog posts and The Annotated Transformer by Sasha Rush were among the few written resources available to understand how these models work. These articles were (and are!) great for developing understanding, but we felt there was a gap in guiding people on how to apply Transformers to industrial use cases. So in 2020, we had the somewhat foolhardy idea to combine the knowledge we'd learned from our jobs as a book. My wife suggested that we contact Thomas to see if he'd be interested in being a co-author, and to our great surprise, he agreed!
Q: Could you summarize the main points covered in the book for the readers?
Lewis: As you might expect from the title, this book is about applying Transformer models to NLP tasks. Most chapters are structured around a single use case you're likely to encounter in the industry. The book covers core applications such as text classification, named entity recognition, and question answering. We take a lot of inspiration from the fantastic fast.ai course (which is how I got started with deep learning!), so the book is written in a hands-on style, emphasising solving real-world problems with code. In the early chapters, we introduce the concepts of self-attention and transfer learning, which underpins the success of Transformers.
The main advice I'd suggest to new writers is to find co-authors or colleagues who can deeply critique your ideas and writing.
The latter part of the book dives into more advanced topics, such as optimising Transformers for production environments and handling scenarios where you have little labelled data (i.e. every data scientist's nightmare
