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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.

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

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