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

Chapter 7: Train Your Own Embeddings

The reason Flair sequence taggers yield such outstanding performance can mainly be attributed to its secret sauce – Flair embeddings. Their contextual design, the fact that they are character-based, and the way they can be used in the backward-forward configuration make them a perfect fit for use in sequence labeling tasks. But, up until this point in this book, we haven't focused much on how these embeddings are actually trained.

In the previous chapter, where we covered model training and learned about word embeddings, we simply used the pre-trained embeddings that were available as part of the Flair Python package. But there are many Natural Language Processing (NLP) problems we may stumble upon where the pre-trained embeddings will not be sufficient.

When working with flair, you may find yourself dealing with a language that isn't covered by Flair's pre-trained embeddings yet, or you may simply need embeddings that...