In this chapter, we took a look at how to integrate fastText word vectors into either linear machine learning models or deep learning models created in Keras, TensorFlow, and PyTorch. You also saw how word vectors can be easily assimilated into existing neural architectures that you might be using in your business application. If you are initializing the embeddings from random values, I would highly recommend that you try to initialize them using fastText values, and then see whether there are performance improvements in your model.
fastText Quick Start Guide
By :
fastText Quick Start Guide
By:
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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Current Title:
fastText Quick Start Guide
Table of Contents (14 chapters)
Preface
Introducing FastText
Creating Models Using FastText Command Line
The FastText Model
Word Representations in FastText
Sentence Classification in FastText
Using FastText in Your Own Models
FastText in Python
Machine Learning and Deep Learning Models
Deploying Models to Web and Mobile
Notes for the Readers
Other Books You May Enjoy
Customer Reviews