Joydeep Bhattacharjee - fastText Quick Start Guide: Get started with Facebook’s library for text representation and classification

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Joydeep Bhattacharjee is a Principal Engineer who works for Nineleaps Technology Solutions. After graduating from National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he stumbled upon machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He also maintains a machine learning blog on Medium .

Krishna Modi is Managing Director at CodeAngle Technologies Pvt. Ltd.

Before starting his company, he worked with enterprises like Cisco Systems and Hewlett Packard Enterprises as a Consultant. He was also an SME for Cloud and Infrastructure Management at Fork Media Pvt. Ltd. Krishna has interest in Data Science and System Programming. He is a registered teaching faculty with ICAI and has been a speaker at various national and international technical sessions hosted by ICAI.

Krishna enjoys writing for technical magazines like PC Quest, Open Source For You and has contributed multiple articles on technology updates for AskMen India Edition.

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FastText is a state-of-the-art tool that can be used to perform text classification and build efficient word representations. It is open source and is designed at Facebook Artificial Intelligence Research (FAIR) lab. It is written in C++, and you also have wrappers available in Python.

This book has the ambitious goal of covering all and techniques and know-how that you need to build NLP applications in the real world. It will also cover the algorithms on which fastText is built so that you will clearly understand the context in which you can expect the best results from fastText.

This book will be of benefit to you if you are a software developer/machine learning engineer trying to understand the state-of-the-art in NLP. A large part of the book deals with real-life problems and considerations for creating an NLP pipeline. If you are an NLP researcher, there is a lot of value here because you will learn about the internal algorithms and considerations taken while developing the fastText software. All the code examples are written in Jupyter Notebooks. I highly recommend you type them out, change them, and tinker with them. Keep the code handy so that you can use it later in your actual projects.

, Introducing FastText , introduces fastText and the NLP context in which this library is useful. It will map the motivations behind building the library and the intended usage and benefits that the creators of the library intended to bring into NLP and the field of computational linguistics. There will also be specific instructions explaining how to install fastText on your work machine. Upon completion of this chapter, you will have fastText installed and running on your computer.