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fastText Quick Start Guide

fastText Quick Start Guide

By : Joydeep Bhattacharjee
3.7 (3)
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fastText Quick Start Guide

fastText Quick Start Guide

3.7 (3)
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)
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1
First Steps
4
The FastText Model
7
Using FastText in Your Own Models
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Using FastText in Your Own Models

In this section, you will learn how to use fastText models in conjunction with other popular NLP libraries, such as Gensim and spaCy. You will also learn how to make fastText part of your pipeline, which will probably include these other tools as well.

The Chapter 5, FastText in Python, is about creating models in Python, either using the official Python bindings for fastText, or you can choose to use the Gensim library, which is a popular Python library for NLP.

In the Chapter 6, Machine Learning and Deep Learning Models, you will understand how to integrate fastText into your NLP pipeline if you have pipelines already built using either the statistical machine learning or deep learning paradigms. In the case of statistical machine learning, this chapter takes the example of the scikit-learn library, and in the case of deep learning, Keras, Tensorflow...

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