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

PyTorch

Following the same logic as the previous two libraries, you can use the torch.nn.EmbeddingBag class to inject the pretrained embeddings. There is a small drawback though. Keras and TensorFlow make the assumption that your tensors are actually implemented as NumPy arrays, while in the case of PyTorch, that's not the case. PyTorch implements the torch tensor. Generally, this is not an issue, but this means that you will need to write your own text conversion and tokenizing pipelines. To circumvent all this rewriting and reinvention of the wheel, you can use the torchtext library.

The torchtext library

The torchtext is an excellent library that takes care of most of the preprocessing steps that you need to build...