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fastText Quick Start Guide

You're reading from fastText Quick Start Guide Get started with Facebook's library for text representation and classification

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781789130997

Length 194 pages

Edition 1st Edition

Languages

Python

Tools

fastText

Concepts

Mobile Application Development

Author (1):

Joydeep Bhattacharjee

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Preface

1. Introducing FastText FREE CHAPTER

2. Creating Models Using FastText Command Line

3. Word Representations in FastText

4. Sentence Classification in FastText

5. FastText in Python

6. Machine Learning and Deep Learning Models

7. Deploying Models to Web and Mobile

1. Notes for the Readers

2. References

3. Other Books You May Enjoy

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Index

Sentence classification

Sentence classification deals with understanding text found in natural languages and determining the classes that it may belong to. In the text classification set of problems, you will have a set of documents d that belongs to the corpus X (which contains all the documents). You will also have a set of finite classes C = {c₁ , c₂, ..., c_n}. Classes are also called categories or labels. To train a model, you would need a classifier, which is generally a well-tested algorithm (not necessary but in this case we will be talking about a well-tested algorithm that is used in fastText) and you will need a corpus with documents and associated labeling identifying the classes that each document belongs to.

Text classification has many practical uses, such as the following:

Creating spam classifiers in email
Page ranking and indexing in search engines
Sentiment detection in reviews that will give an idea whether customers are happy with the product or not

Text classification...

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Authors (1)

Joydeep Bhattacharjee

Joydeep Bhattacharjee

Joydeep Bhattacharjee is a Principal Engineer who works for Nineleaps Technology Solutions. After graduating from National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he stumbled upon machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He also maintains a machine learning blog on Medium.

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