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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 efficient data insights requires powerful NLP tools like fastText. This book is your i...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; First Steps; Section 1: First Steps; Chapter 1: Introducing FastText; Introducing fastText; Installing fastText; Prerequisites; Windows; Linux; Installing dependencies on RHEL machines supporting the yum package manager; Installing dependencies on Debian-based machines such as Ubuntu; Installing dependencies on Arch Linux using pacman; Installing dependencies on Mac systems; Installing Python dependencies; Installing fastText on Windows; Installing fastText in Linux and macOS.
- Using a Docker image for fastTextSummary; Chapter 2: Creating Models Using FastText Command Line; Section 2: The FastText Model; Chapter 3: Word Representations in FastText; Chapter 4: Sentence Classification in FastText; Section 3: Using FastText in Your Own Models; Chapter 5: FastText in Python; FastText official bindings; PyBind; Preprocessing data; Unsupervised learning; Training in fastText; Evaluating the model; Word vectors; Nearest neighbor queries; Word similarity; Model performance; Model visualization; Supervised learning; Data preprocessing and normalization; Training the model.
- PredictionTesting the model; Confusion matrix; Gensim; Training a fastText model; Hyperparameters; Model saving and loading; Word vectors; Model Evaluation; Word Mover's Distance; Getting more out of the training process; Machine translation using Gensim; Summary; Chapter 6: Machine Learning and Deep Learning Models; Scikit-learn and fastText; Custom classifiers for fastText; Bringing the whole thing together; Embeddings; Keras; Embedding layer in Keras; Convolutional neural networks; TensorFlow; Word embeddings in TensorFlow; RNN architectures; PyTorch; The torchtext library.
- Data classes in torchtextUsing the iterators; Bringing it all together; Summary; Chapter 7: Deploying Models to Web and Mobile; Deploying to the web; Flask; The fastText functions; The flask endpoints; Deploying to smaller devices; Prerequisites
- Completing the Google tutorial; App considerations; Adding the fastText model; FastText in Java; Adding the library dependencies to Android; Using library dependencies in Android; Finally the app; Summary; Appendix A: Notes for the Readers; Windows and Linux; Python 2 and Python 3; The fastText command line; The fastText supervised.
- The fastText skipgram The fastText cbow; Gensim fastText parameters; Appendix B: References; Other Books You May Enjoy; Index.