Book Image

fastText Quick Start Guide

By : Joydeep Bhattacharjee
Book Image

fastText Quick Start Guide

By: Joydeep Bhattacharjee

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.
Table of Contents (14 chapters)
Free Chapter
1
First Steps
4
The FastText Model
7
Using FastText in Your Own Models

Deploying to smaller devices

As you saw in Chapter 2, Creating Models Using FastText Command Line, you can create a compressed fastText model from a whole model using a command similar to this one:

$ ./fasttext quantize -output <model prefix> -input <training file> -qnorm -retrain -epoch <number of epochs> -cutoff <number of words to consider>

In Chapter 4, Sentence Classification in FastText, we also revisited the concept of having compressed models and how compression was achieved without much loss in performance.

This enables you to deploy machines in smaller devices as well. One of the first things that comes to mind is whether the files can be packaged with an Android app and deployed in an Android application.

In this section, I will put into place all the requirements and dependencies that should enable you to deploy an Android fastText application...