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Natural Language Processing with Flair

Natural Language Processing with Flair

By : Magajna
5 (8)
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Natural Language Processing with Flair

Natural Language Processing with Flair

5 (8)
By: 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)
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Part 1: Understanding and Solving NLP with Flair
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Chapter 1: Introduction to Flair
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Part 2: Deep Dive into Flair – Training Custom Models
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Part 3: Real-World Applications with Flair
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Working with text classifiers that require little to no training data

When mentioning Flair and its strengths in the previous chapters, we mainly focused on various powerful ways of solving sequence labeling tasks. When talking about text classification, however, Flair was generally presented as a decent text classification tool, although nothing special compared to its sequence labeling capabilities. This was indeed the case – until now. Flair recently introduced a novel text classification method called TARS. The concept is described in depth in the Task-Aware Representation of Sentences for Generic Text Classification paper available at https://aclanthology.org/2020.coling-main.285/, which is well worth a read.

Transformer-based text classifiers in Flair leverage a special linear layer on top of the transformer model to produce the class probability distributions. The first problem with this approach is that when new class labels are introduced to the problem, or when...

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