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fastText Quick Start Guide

By : Joydeep Bhattacharjee

3.7 (3)

Buy this Book

fastText Quick Start Guide

3.7 (3)

By: Joydeep Bhattacharjee

Buy this Book

Overview of this book

Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects.

Preface

Who this book is for

What this book covers

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Free Chapter

First Steps

Introducing FastText

Introducing fastText

Installing fastText

Installing dependencies on Mac systems

Installing Python dependencies

Using a Docker image for fastText

Summary

Creating Models Using FastText Command Line

Text classification using fastText

FastText word vectors

Summary

The FastText Model

Word Representations in FastText

Word-to-vector representations

Summary

Sentence Classification in FastText

Sentence classification

fastText supervised learning

fastText model quantization

Summary

Using FastText in Your Own Models

FastText in Python

FastText official bindings

Gensim

Summary

Machine Learning and Deep Learning Models

Scikit-learn and fastText

Embeddings

Keras

TensorFlow

PyTorch

Summary

Deploying Models to Web and Mobile

Deploying to the web

Deploying to smaller devices

Summary

Notes for the Readers

Windows and Linux

Python 2 and Python 3

The fastText command line

Gensim fastText parameters

References

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

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Introducing fastText

In today's interconnected world, a lot of text data gets generated around the world. This text information includes descriptions of things. Take, for example, people writing about products in Amazon reviews, or people writing about their thoughts through their Facebook posts. Natural Language Processing (NLP) is the application of machine learning and other computational techniques to understanding and representating spoken and written text. The following are the major challenges that NLP seeks to solve:

Topic modeling: In general, texts deal with a topic. Topic modeling is frequently used to determine hidden structures or "abstract topics" that may be present in a collection of documents. An effective application of topic modeling would be summarization. For example, legal documents are quite complex and verbose, and hence systems such as...