You're reading from Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781838641351

Length 514 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Machine Learning

Author (1):

Thushan Ganegedara

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Table of Contents (15) Chapters

Preface

1. Introduction to Natural Language Processing FREE CHAPTER

2. Understanding TensorFlow 2

3. Word2vec – Learning Word Embeddings

4. Advanced Word Vector Algorithms

5. Sentence Classification with Convolutional Neural Networks

6. Recurrent Neural Networks

7. Understanding Long Short-Term Memory Networks

8. Applications of LSTM – Generating Text

9. Sequence-to-Sequence Learning – Neural Machine Translation

10. Transformers

11. Image Captioning with Transformers

12. Other Books You May Enjoy

13. Index

Appendix A: Mathematical Foundations and Advanced TensorFlow

Preparing data for the NMT system

In this section, we will understand the data and learn about the process for preparing data for training and predicting from the NMT system. First, we will talk about how to prepare training data (that is, the source sentence and target sentence pairs) to train the NMT system, followed by inputting a given source sentence to produce the translation of the source sentence.

The dataset

The dataset we’ll be using for this chapter is the WMT-14 English-German translation data from https://nlp.stanford.edu/projects/nmt/. There are ~4.5 million sentence pairs available. However, we will use only 250,000 sentence pairs due to computational feasibility. The vocabulary consists of the 50,000 most common English words and the 50,000 most common German words, and words not found in the vocabulary will be replaced with a special token, <unk>. You will need to download the following files:

train.de – File containing German...

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You're reading from Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

Table of Contents (15) Chapters

Preparing data for the NMT system

The dataset

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Authors (1)