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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