Book Image

Natural Language Processing with TensorFlow - Second Edition

By : Thushan Ganegedara
2 (1)
Book Image

Natural Language Processing with TensorFlow - Second Edition

2 (1)
By: Thushan Ganegedara

Overview of this book

Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Table of Contents (15 chapters)
12
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Index

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...