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

The motivation behind training custom models

In this book we so far discussed the features and models that are available in Flair straight out of the box. However, if you work with NLP long enough, you will likely encounter a sequence labeling problem that is complex or specific enough that there will be no pre-trained models available out there. This can happen in either of the following situations:

  • The problem you are solving is domain-specific: Sequence taggers such as Named Entity Recognition (NER) or Part-of-Speech (PoS) taggers are usually trained on large, generic corpora that are supposed to represent the general use of a language. But if our problem is domain-specific, it's likely that we will require a custom tagger with domain-specific labels trained on domain-specific corpora.
  • A pre-trained model exists but doesn't perform well enough: Every model made available in Flair and the approaches used for training are usually reviewed by other contributors...