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Full Description
Perform efficient fast text representation and classification with Facebook's fastText library
Key Features
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
Book DescriptionFacebook'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
Who this book is forThis book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.
Contents
Table of Contents
Introducing FastText
Creating Models Using FastText Command Line
Word Representations in FastText
Sentence Classification in FastText
FastText in Python
Machine Learning and Deep Learning Models
Deploying Models to Web and Mobile
