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

TensorFlow

TensorFlow is a computation library developed by Google. It is quite popular now and is used by many companies to create their neural network models. After what you have seen in Keras, the logic behind augmenting TensorFlow models using fastText is the same.

Word embeddings in TensorFlow

To create word embeddings in TensorFlow, you will need to create an embeddings matrix where all the tokens in your list of documents have unique IDs, and so each document is a vector of these IDs. Now, let's say you have an embedding in a NumPy array called word_embedding, with vocab_size rows and embedding_dim columns, and you want to create a tensor W. Taking a specific example, the sentence "I have a cat." can...