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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

Leave a review - let other readers know what you think

Index

FastText official bindings

The steps to install the official bindings for Python are covered in the first chapter. In this section, we will cover how to use the official fastText Python package to train, load, and use the models.

Using the Python fastText library, you will be able to implement all the necessary features that can be done using the command line. Lets take a look at the ways to implement unsupervised and supervised learning using Python fastText.

Note

Note: In this chapter, we will be using Python3 and so the code examples will be in that. For users who are using Python2, please take a look at the Appendix for notes on the considerations that you need to bear in mind when using Python2.

PyBind

Python bindings for fastText are made using the excellent PyBind library. PyBind is a lightweight library meant to expose C++ types in Python and vice versa, making it an excellent choice for creating the Python bindings for fastText. It supports almost all the popular C++ compilers such as...

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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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