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.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
First Steps
First Steps
2
Introducing FastText
Introducing FastText
Introducing fastText
Installing fastText
Installing dependencies on Mac systems
Installing Python dependencies
Using a Docker image for fastText
Summary
3
Creating Models Using FastText Command Line
Creating Models Using FastText Command Line
Text classification using fastText
FastText word vectors
Summary
4
The FastText Model
The FastText Model
5
Word Representations in FastText
Word Representations in FastText
Word-to-vector representations
Summary
6
Sentence Classification in FastText
Sentence Classification in FastText
Sentence classification
fastText supervised learning
fastText model quantization
Summary
7
Using FastText in Your Own Models
Using FastText in Your Own Models
8
FastText in Python
FastText in Python
FastText official bindings
Gensim
Summary
9
Machine Learning and Deep Learning Models
Machine Learning and Deep Learning Models
Scikit-learn and fastText
Embeddings
Keras
TensorFlow
PyTorch
Summary
10
Deploying Models to Web and Mobile
Deploying Models to Web and Mobile
Deploying to the web
Deploying to smaller devices
Summary
11
Notes for the Readers
Notes for the Readers
Windows and Linux
Python 2 and Python 3
The fastText command line
Gensim fastText parameters
12
References
References
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
13
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Installing Python dependencies

I recommend that you install Anaconda so that there are no issues with installing Python and using it for fastText. Detailed instructions for installing Anaconda are given on the official documentation page, which can be accessed at https://conda.io/docs/user-guide/install/linux.html. Simply stated, if you are on Windows, then download the Windows installer, double-click on it, and then follow the instructions on the screen. Installing it using a GUI is also possible for macOS.

In the case of Linux and macOS, download the corresponding bash file and then run the following command in a Terminal:

$ bash downloadedfile.sh

Please take care to download and install it using installers that are tagged for Python 3.x. The Python code snippets that will be shown in this book will be shown for Python 3.x.

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