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

Summary

In this chapter, we covered Flair's base types, such as the Sentence and Token objects, explained how to initialize and use them, and tried out some of their basic helper methods. This should allow us to handle, transform, and understand data in Flair more easily as we move toward more complex topics. We also covered using custom tokenizers in Flair and implemented our own character-based tokenizer. Finally, we scraped the surface of what Flair's datasets and the Corpus objects can do. We learned how to load corpora and datasets, assess their size, extract, and read individual sentences, and downsample entire datasets.

We are now familiar enough with the syntax, basic objects and helper methods to be able to move on to Flair's most powerful NLP technique – sequence tagging. We will cover this in the next chapter.