Natural language processing is one of the leading domains of AI that involves enabling computers to understand human language.
To realize the full potential of NLP, however, you need to be able to use the latest Transformer architecture—a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data.
This may seem like a difficult task, but there are plenty of tools available that make working with NLP models much simpler. One of them is Hugging Face, a library that deserves a special mention.
Hugging Face allows you to shorten the distance to the latest NLP solutions and technologies, and also have some fun while doing it. Although the library seems to be a rather complicated tool, it’s not like that at all. In fact, it can prove really useful in NLP work for both beginners and more advanced users.
We will start our article by gaining theoretical knowledge of modern NLP architectures and ideas, then move on to a practical task in which we’ll build a text classifier using Hugging Face and all the theoretical elements we have learned. Read on!