fastText Quick Start Guide: Get started with Facebook’s library for text representation and classification
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.
1: INTRODUCING FASTTEXT
2: CREATING MODELS USING FASTTEXT COMMAND LINE
3: WORD REPRESENTATIONS IN FASTTEXT
4: SENTENCE CLASSIFICATION IN FASTTEXT
5: FASTTEXT IN PYTHON
6: MACHINE LEARNING AND DEEP LEARNING MODELS
7: DEPLOYING MODELS TO WEB AND MOBILE
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