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Hands-On Natural Language Processing with PyTorch 1.x

You're reading from   Hands-On Natural Language Processing with PyTorch 1.x Build smart, AI-driven linguistic applications using deep learning and NLP techniques

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Essentials of PyTorch 1.x for NLP
2. Chapter 1: Fundamentals of Machine Learning and Deep Learning FREE CHAPTER 3. Chapter 2: Getting Started with PyTorch 1.x for NLP 4. Section 2: Fundamentals of Natural Language Processing
5. Chapter 3: NLP and Text Embeddings 6. Chapter 4: Text Preprocessing, Stemming, and Lemmatization 7. Section 3: Real-World NLP Applications Using PyTorch 1.x
8. Chapter 5: Recurrent Neural Networks and Sentiment Analysis 9. Chapter 6: Convolutional Neural Networks for Text Classification 10. Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks 11. Chapter 8: Building a Chatbot Using Attention-Based Neural Networks 12. Chapter 9: The Road Ahead 13. Other Books You May Enjoy

NLP for PyTorch

Now that we have learned how to build neural networks, we will see how it is possible to build models for NLP using PyTorch. In this example, we will create a basic bag-of-words classifier in order to classify the language of a given sentence.

Setting up the classifier

For this example, we'll take a selection of sentences in Spanish and English:

  1. First, we split each sentence into a list of words and take the language of each sentence as a label. We take a section of sentences to train our model on and keep a small section to one side as our test set. We do this so that we can evaluate the performance of our model after it has been trained:
    ("This is my favourite chapter".lower().split(),\
     "English"),
    ("Estoy en la biblioteca".lower().split(), "Spanish")

    Note that we also transform each word into lowercase, which stops words being double counted in our bag-of-words. If we have the word book and the word Book...

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