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

Other embeddings in Flair

In this chapter, we covered most of the embedding types and techniques responsible for Flair's state-of-the art performance, though there are still many other embedding types left unexplained in this book.

These include the following:

  • ELMoEmbeddings: Contextualized embeddings using a bidirectional LSTM
  • OneHotEmbeddings: Embeddings where each word is a one-hot vector
  • BytePairEmbeddings: Embeddings precomputed on the subword-level
  • TransformerWordEmbeddings: Embeddings using transformer-based architectures
  • CharacterEmbeddings: Character-level embeddings that are randomly set each time you initialize the class

While the inner workings of these embeddings exceed the scope of this book, even without fully understanding how they work, you should now be able to use them the same way you use any other embedding object in Flair as they all share the same interface.