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
First Steps
The FastText Model
Using FastText in Your Own Models

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...