Book Image

Natural Language Processing with Flair

By : Tadej Magajna
Book Image

Natural Language Processing with Flair

By: Tadej Magajna

Overview of this book

Flair is an easy-to-understand natural language processing (NLP) framework designed to facilitate training and distribution of state-of-the-art NLP models for named entity recognition, part-of-speech tagging, and text classification. Flair is also a text embedding library for combining different types of embeddings, such as document embeddings, Transformer embeddings, and the proposed Flair embeddings. Natural Language Processing with Flair takes a hands-on approach to explaining and solving real-world NLP problems. You'll begin by installing Flair and learning about the basic NLP concepts and terminology. You will explore Flair's extensive features, such as sequence tagging, text classification, and word embeddings, through practical exercises. As you advance, you will train your own sequence labeling and text classification models and learn how to use hyperparameter tuning in order to choose the right training parameters. You will learn about the idea behind one-shot and few-shot learning through a novel text classification technique TARS. Finally, you will solve several real-world NLP problems through hands-on exercises, as well as learn how to deploy Flair models to production. By the end of this Flair book, you'll have developed a thorough understanding of typical NLP problems and you’ll be able to solve them with Flair.
Table of Contents (15 chapters)
1
Part 1: Understanding and Solving NLP with Flair
6
Part 2: Deep Dive into Flair – Training Custom Models
11
Part 3: Real-World Applications with Flair

Understanding sequence tagging in Flair

Before diving straight into how to use sequence taggers in Flair, let's first briefly explain how sequence tagging (also known as sequence labeling) works. From an engineering point of view, a sequence tagger is a tool that receives text as input and returns a list of assignments, where each assignment includes a tag name and a span indicating where the annotated unit of text (usually a word) begins and ends.

The architecture and design of sequence labeling in Flair can best be explained with the following diagram:

Figure 4.1– Overview of sequence tagging in Flair

The preceding diagram displays the sequence tagging architecture consisting of two components: the language model (in yellow) and the sequence tagging (labeling) model (in blue). The bidirectional character language model receives the original text as input and computes contextual embeddings () for each word. These are then passed into a BiLSTM...