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fastText Quick Start Guide
fastText Quick Start Guide

fastText Quick Start Guide: Get started with Facebook's library for text representation and classification

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Profile Icon Joydeep Bhattacharjee
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€8.99 €19.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
eBook Jul 2018 194 pages 1st Edition
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€8.99 €19.99
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Arrow left icon
Profile Icon Joydeep Bhattacharjee
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€8.99 €19.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
eBook Jul 2018 194 pages 1st Edition
eBook
€8.99 €19.99
Paperback
€24.99
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Free Trial
Renews at €18.99p/m
eBook
€8.99 €19.99
Paperback
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Renews at €18.99p/m

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fastText Quick Start Guide

Introducing FastText

Welcome to fastText Quick Start Guide. In this first chapter, you will find out how to install fastText and create a stable environment in which to learn how to use fastText applications as part of your Natural Language Processing applications.

fastText is a library that helps you to generate efficient word representations and gives you support for text classification out of the box. In this book, we will take a look at a specific use case, namely machine translation, and use fastText for that. We have chosen machine translation because fastText claims that it is superior in terms of yet unknown words, and can handle different languages for which sufficiently large data sources and corpora may not be available. In different chapters, we will see how fastText fares in such cases. General techniques will also be discussed so that you will be able to extend those...

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...

Installing fastText

Depending on your operating system, you will need to make sure that you have some dependencies installed in your machine. In this section, you will get to know how to install fastText based on whether you are using a Linux, Windows, or macOS operating system. Additionally, you will get to know what additional dependencies you should install depending on your usage. My recommendation is to install all the software packages, as we will be exploring all the various ways we can use fastText in this book.

Prerequisites

FastText works on Windows, Linux, and macOS. FastText is built using the C++ language, so you will first need a good C++ compiler.

...

Installing dependencies on Mac systems

On macOS, you should have Clang installed by default, which is designed to be a drop-in replacement for the normal compilers for C, C++, and other similar languages. Check whether the version is 3.3 or later using clang --version in a Terminal. If you do not have Clang or something from the older versions, then you can install using the xcode command-line tools using a Terminal:

$ xcode-select --install

A dialog should appear next that asks if you want to install the developer tools. Click on the Install button.

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.

...

Using a Docker image for fastText

You can also use Docker to run fastText on your machine and not worry about building it. This can be done to maintain version control between specific versions and thus gives us predictability and consistency. You can get information on how to install Docker from the following link: https://docs.docker.com/install/#cloud.

After installing, start the Docker service before running the following commands:

 start the docker service.
$ systemctl start docker

# run the below commands to start the fasttext container.
$ docker pull xebxeb/fasttext-docker

You should now be able to run fastText:

$ mkdir -p /tmp/data && mkdir -p /tmp/result
$ docker run --rm -v /tmp/data:/data -v /tmp/result:/result \
-it xebxeb/fasttext-docker ./classification-example.sh

You may need to provide permissions and create the specific directories to run the...

Summary

In this chapter, you have taken a look at how to install and start using fastText in the environment of your choice.

In the next chapter, we will be taking a look at how to train fastText models using the command line and how to use them.

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Key benefits

  • Introduction to Facebook's fastText library for NLP
  • Perform efficient word representations, sentence classification, vector representation
  • Build better, more scalable solutions for text representation and classification

Description

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.

Who is this book for?

This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.

What you will learn

  • Create models using the default command line options in fastText
  • Understand the algorithms used in fastText to create word vectors
  • Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline
  • Explore word representation and sentence classification using fastText
  • Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently
  • Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch

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Publication date : Jul 26, 2018
Length: 194 pages
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Language : English
ISBN-13 : 9781789136715
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Publication date : Jul 26, 2018
Length: 194 pages
Edition : 1st
Language : English
ISBN-13 : 9781789136715
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Category :
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Tools :

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Table of Contents

13 Chapters
First Steps Chevron down icon Chevron up icon
Introducing FastText Chevron down icon Chevron up icon
Creating Models Using FastText Command Line Chevron down icon Chevron up icon
The FastText Model Chevron down icon Chevron up icon
Word Representations in FastText Chevron down icon Chevron up icon
Sentence Classification in FastText Chevron down icon Chevron up icon
Using FastText in Your Own Models Chevron down icon Chevron up icon
FastText in Python Chevron down icon Chevron up icon
Machine Learning and Deep Learning Models Chevron down icon Chevron up icon
Deploying Models to Web and Mobile Chevron down icon Chevron up icon
Notes for the Readers Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Laxmi Vanam Sep 24, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having followed Joydeep's lectures (youtube channel and seminars) for a while, I got this book hoping to get an indepth understanding of the fasttext library while working on my NLP project. I would say, it absolutely met my expectations in taking me from zero-in-depth understanding of the concepts. I have always believed that books are deeper way to dive into the subject compared to online lectures and this book proved to be right. I can say with just the super basic knowledge of what NLP is you can rely on this book to take you to a next level. All the concepts are explained in a lucid manner and can me sense to an absolute beginner in NLP. I would recommend this book to anyone who wants to get absolute understanding of fastText library for text classification on both supervised and unsupervised representations.
Amazon Verified review Amazon
MJ Oct 23, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been using fastText for about 6 months now. I had not been able to find any good resources that simply explained all of the necessary steps to prepare text training data before building a FastText model. It was also very hard to know how to tweak the supervised hyperparameters. This books answered my questions!I'm so thankful for Joydeep Bhattacharjee and his hard work on this fantastic resource.
Amazon Verified review Amazon
C. Vogel May 10, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Bash scripts, really?By today standards, one would expect a clean support of notebooks or organized code per chapter. Not a mixed bag of 5 lines Python functions, and command line scripts.
Amazon Verified review Amazon
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