Chapter 1: Introduction to Flair
There are few Natural Language Processing (NLP) frameworks out there as easy to learn and as easy to work with as Flair. Packed with pre-trained models, excellent documentation, and readable syntax, it provides a gentle learning curve for NLP researchers who are not necessarily skilled in coding; software engineers with poor theoretical foundations; students and graduates; as well as individuals with no prior knowledge simply interested in the topic. But before diving straight into coding, some background about the motivation behind Flair, the basic NLP concepts, and the different approaches to how you can set up your local environment may help you on your journey toward becoming a Flair NLP expert.
In Flair's official GitHub README, the framework is described as:
"A very simple framework for state-of-the-art Natural Language Processing"
This description will raise a few eyebrows. NLP researchers will immediately be interested in knowing what specific tasks the framework achieves its state-of-the-art results in. Engineers will be intrigued by the very simple label, but will wonder what steps are required to get up and running and what environments it can be used in. And those who are not knowledgeable in NLP will wonder whether they will be able to grasp the knowledge required to understand the problems Flair is trying to solve.
In this chapter, we will be answering all of these questions by covering the basic NLP concepts and terminology, providing an overview of Flair, and setting up our development environment with the help of the following sections:
- A brief introduction to NLP
- What is Flair?
- Getting ready