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On completion of this section, the reader will have working knowledge of how to install fastText and run the command line application effectively on any dataset.

In the Chapter 1, Introducing fastText, you will get a description of fastText and the Natural Language Processing (NLP) context in which this library is useful. It will map the motivations behind building the library, its intended use, and the benefits that the creators of the library wanted to bring to NLP and the field of Computational Linguistics. Furthermore, there will be specific instructions on how to install fastText on the reader's work machine. On completion of this chapter, the reader will have fastText installed and running on their computer.

In the Chapter 2, Creating Models Using the FastText Command Line, you will get to know about the rich command line that the fastText library provides. This chapter will give you descriptions of the default command line options and how to use them to create models. If the reader only has a superficial interest in fastText, reading till this chapter should be enough.

Key benefits

Introduction to Facebook's fastText library for NLP

Perform efficient word representations, sentence classification, vector representation

Build better, more scalable solutions for text representation and classification

Description

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.

What you will learn

Create models using the default command line options in fastText

Understand the algorithms used in fastText to create word vectors

Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline

Explore word representation and sentence classification using fastText

Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently

Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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Natural Language Processing and Computational Linguistics

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Total € 90.97

Laxmi Vanam Sep 24, 2018

Having followed Joydeep's lectures (youtube channel and seminars) for a while, I got this book hoping to get an indepth understanding of the fasttext library while working on my NLP project. I would say, it absolutely met my expectations in taking me from zero-in-depth understanding of the concepts. I have always believed that books are deeper way to dive into the subject compared to online lectures and this book proved to be right. I can say with just the super basic knowledge of what NLP is you can rely on this book to take you to a next level. All the concepts are explained in a lucid manner and can me sense to an absolute beginner in NLP. I would recommend this book to anyone who wants to get absolute understanding of fastText library for text classification on both supervised and unsupervised representations.

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MJ Oct 23, 2018

I've been using fastText for about 6 months now. I had not been able to find any good resources that simply explained all of the necessary steps to prepare text training data before building a FastText model. It was also very hard to know how to tweak the supervised hyperparameters. This books answered my questions!I'm so thankful for Joydeep Bhattacharjee and his hard work on this fantastic resource.

C. Vogel May 10, 2020

Bash scripts, really?By today standards, one would expect a clean support of notebooks or organized code per chapter. Not a mixed bag of 5 lines Python functions, and command line scripts.

fastText Quick Start Guide: Get started with Facebook's library for text representation and classification

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