Preface

Natural Language Processing (NLP) is currently one of the fastest-growing subfields of AI where new advances or changes to NLP frameworks happen daily. Anyone new to the field will find it difficult to know where to start, which ideas are outdated, which ones are still relevant, and which are soon to become state of the art. Similarly, people with a solid theoretical NLP background aiming to gain practical experience in NLP will find it difficult to choose the right NLP library or framework given the oversupply of different options currently available in the open source community. As someone who has used Flair both professionally as well as for personal projects, I would find it hard to recommend any other framework that is as intuitive, as relevant, and as packed with ready-to-use, state-of-the-art models as Flair.

The most interesting thing about Flair may not be its simplicity, performance, or ease of use. Instead, it has to do with how Flair was originally designed. The first release was never intended to serve as a full-fledged NLP framework. The first release of Flair (v0.1), at heart, was merely a tool that served as a real-world implementation of Flair embeddings – the underlying concept that gives Flair sequence labeling models their amazing performance. It wasn't until later Flair versions that the library introduced Transformer models, text classification models, and other tools that you would normally expect from an established NLP framework.

Anyone aiming to build real-world NLP solutions will need to juggle between spending time learning more about NLP theory and investing effort in choosing the right engineering approach. This book will help you find the right balance between both. It will arm you with just the right amount of theoretical foundation to help understand the underlying NLP concepts as well as help you gain enough engineering knowledge and experience so that you can use Flair proficiently.