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.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Part 1: Understanding and Solving NLP with Flair
Part 1: Understanding and Solving NLP with Flair
Free Chapter
2
Chapter 1: Introduction to Flair
Chapter 1: Introduction to Flair
Technical requirements
A brief introduction to NLP
Introducing Flair
Setting up the development environment
Summary
3
Chapter 2: Flair Base Types
Chapter 2: Flair Base Types
Technical requirements
Sentence and Token objects
Using custom tokenizers
Understanding the Corpus object
Summary
4
Chapter 3: Embeddings in Flair
Chapter 3: Embeddings in Flair
Technical requirements
Understanding word embeddings
Classic word embeddings in Flair
Flair embeddings
Stacked embeddings
Document embeddings
Other embeddings in Flair
Summary
5
Chapter 4: Sequence Tagging
Chapter 4: Sequence Tagging
Technical requirements
Understanding sequence tagging in Flair
Named entity recognition in Flair
Part-of-speech tagging in Flair
Sequence labeling metrics
Summary
6
Part 2: Deep Dive into Flair – Training Custom Models
Part 2: Deep Dive into Flair – Training Custom Models
7
Chapter 5: Training Sequence Labeling Models
Chapter 5: Training Sequence Labeling Models
Technical requirements
The motivation behind training custom models
Understanding the hardware requirements for training models
Working with parameters for training and evaluation
Training custom sequence labeling models
Knowing when to stop and try again
Summary
8
Chapter 6: Hyperparameter Optimization in Flair
Chapter 6: Hyperparameter Optimization in Flair
Technical requirements
Understanding hyperparameter tuning
Hyperparameter tuning in Python
Hyperparameter optimization in Flair
Summary
9
Chapter 7: Train Your Own Embeddings
Chapter 7: Train Your Own Embeddings
Technical requirements
Understanding the how and why behind custom Flair embeddings
Evaluating word embeddings
Training Flair embeddings on the world's smallest language
Summary
10
Chapter 8: Text Classification in Flair
Chapter 8: Text Classification in Flair
Technical requirements
Understanding text classification
Text classification in Flair
Training a text classifier in Flair
Working with text classifiers that require little to no training data
Summary
11
Part 3: Real-World Applications with Flair
Part 3: Real-World Applications with Flair
12
Chapter 9: Deploying and Using Models in Production
Chapter 9: Deploying and Using Models in Production
Technical requirements
Technical considerations for NLP models in production
Self-serving flair models
Using managed services for deploying and using Flair models
Summary
13
Chapter 10: Hands-On Exercise – Building a Trading Bot with Flair
Chapter 10: Hands-On Exercise – Building a Trading Bot with Flair
Technical requirements
Understanding the trading strategy
Implementing the Flair trading bot
A Flair coding cheat sheet
Summary
Why subscribe?
14
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Chapter 1: Introduction to Flair

There are few Natural Language Processing (NLP) frameworks out there as easy to learn and as easy to work with as Flair. Packed with pre-trained models, excellent documentation, and readable syntax, it provides a gentle learning curve for NLP researchers who are not necessarily skilled in coding; software engineers with poor theoretical foundations; students and graduates; as well as individuals with no prior knowledge simply interested in the topic. But before diving straight into coding, some background about the motivation behind Flair, the basic NLP concepts, and the different approaches to how you can set up your local environment may help you on your journey toward becoming a Flair NLP expert.

In Flair's official GitHub README, the framework is described as:

"A very simple framework for state-of-the-art Natural Language Processing"

This description will raise a few eyebrows. NLP researchers will immediately be interested in knowing what specific tasks the framework achieves its state-of-the-art results in. Engineers will be intrigued by the very simple label, but will wonder what steps are required to get up and running and what environments it can be used in. And those who are not knowledgeable in NLP will wonder whether they will be able to grasp the knowledge required to understand the problems Flair is trying to solve.

In this chapter, we will be answering all of these questions by covering the basic NLP concepts and terminology, providing an overview of Flair, and setting up our development environment with the help of the following sections:

  • A brief introduction to NLP
  • What is Flair?
  • Getting ready
Previous Section
End of Section 1
Next Section