Natural Language Processing with Flair: A practical guide to understanding and solving NLP problems with Flair
- Length: 200 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2022-04-29
- ISBN-10: 1801072310
- ISBN-13: 9781801072311
- Sales Rank: #0 (See Top 100 Books)
Learn how to solve practical NLP problems with the Flair Python framework, train sequence labeling models, work with text classifiers and word embeddings, and much more through hands-on practical exercises
Key Features
- Backed by the community and written by an NLP expert
- Get an understanding of basic NLP problems and terminology
- Solve real-world NLP problems with Flair with the help of practical hands-on exercises
Book Description
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.
What you will learn
- Gain an understanding of core NLP terminology and concepts
- Get to grips with the capabilities of the Flair NLP framework
- Find out how to use Flair’s state-of-the-art pre-built models
- Build custom sequence labeling models, embeddings, and classifiers
- Learn about a novel text classification technique called TARS
- Discover how to build applications with Flair and how to deploy them to production
Who this book is for
This Flair NLP book is for anyone who wants to learn about NLP through one of the most beginner-friendly, yet powerful Python NLP libraries out there. Software engineering students, developers, data scientists, and anyone who is transitioning into NLP and is interested in learning about practical approaches to solving problems with Flair will find this book useful. The book, however, is not recommended for readers aiming to get an in-depth theoretical understanding of the mathematics behind NLP. Beginner-level knowledge of Python programming is required to get the most out of this book.
Natural Language Processing with Flair
Contributors
About the author
About the reviewers
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
Part 1: Understanding and Solving NLP with Flair
Chapter 1: Introduction to Flair
Technical requirements
A brief introduction to NLP
Tokenization
Text vectorization
Named entity recognition
Word-sense disambiguation
Part-of-speech tagging
Chunking
Stemming and lemmatization
Text classification
Introducing Flair
Setting up the development environment
Creating the virtual environment
Installing a published version of Flair in a virtual environment
Installing directly from the GitHub repository (optional)
Running code that uses Flair
Summary
Chapter 2: Flair Base Types
Technical requirements
Sentence and Token objects
Understanding the Sentence class
Understanding the Token class
Tokenization in Flair
Sentence and Token object helper methods
Using custom tokenizers
Using the TokenizerWrapper class
Understanding the Corpus object
Summary
Chapter 3: Embeddings in Flair
Technical requirements
Understanding word embeddings
Cosine similarity
The "king – man ≈ queen – woman" analogy
Classic word embeddings in Flair
Flair embeddings
Understanding the contextuality of Flair embeddings
Character-level sequence modeling in Flair embeddings
Pooled Flair embeddings
Available Flair embeddings
Stacked embeddings
Document embeddings
Other embeddings in Flair
Summary
Chapter 4: Sequence Tagging
Technical requirements
Understanding sequence tagging in Flair
Named entity recognition in Flair
Types of NER taggers in Flair
Part-of-speech tagging in Flair
Part-of-speech tagging tag sets
Types of PoS taggers in Flair
Other tagging tasks in Flair
Sequence labeling metrics
Measuring accuracy for sequence labeling tasks
Measuring the F1 score for sequence labeling tasks
Summary
Part 2: Deep Dive into Flair – Training Custom 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
Understanding neural model training
Parameters and terminology used in Flair model training
Training custom sequence labeling models
Loading a tagged corpus
Loading the tag dictionary
Building the embedding stack
Initializing the SequenceTagger object
Training the model
Understanding training output files
Loading and using custom Flair models
Knowing when to stop and try again
Monitoring loss
Assessing and comparing performance metrics
Summary
Chapter 6: Hyperparameter Optimization in Flair
Technical requirements
Understanding hyperparameter tuning
Optimization methods
Hyperparameter tuning in Python
Hyperparameter tuning in Python hands-on
Hyperparameter optimization in Flair
Hyperparameter tuning in Flair hands-on
Summary
Chapter 7: Train Your Own Embeddings
Technical requirements
Understanding the how and why behind custom Flair embeddings
Why training embeddings rarely ever means training embeddings
Corpora for Flair embeddings
Dictionaries for training Flair embeddings
Evaluating word embeddings
Training Flair embeddings on the world's smallest language
Preparing the dictionary
Preparing the corpus
Training the language model
Using custom embeddings on downstream tasks
Performing intrinsic evaluation on custom Flair embeddings
Summary
Chapter 8: Text Classification in Flair
Technical requirements
Understanding text classification
Training text classifiers
Text classification in Flair
Using pre-trained Flair text classification models
Document embeddings in Flair
Text classifiers in Flair
Training a text classifier in Flair
Working with text classifiers that require little to no training data
Zero-shot classification with TARS
Few-shot classification with TARS
Summary
Part 3: Real-World Applications with Flair
Chapter 9: Deploying and Using Models in Production
Technical requirements
Technical considerations for NLP models in production
Client-side versus server-side inference
When to use managed services and when to self-serve server-side models
Managed services for Flair models
A model deployment decision-making flowchart
Self-serving flair models
A proof-of-concept self-serve tool for Flair
Using managed services for deploying and using Flair models
The Hugging Face Models Hub
Summary
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
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