Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By : Thomas Dop
Book Image

Hands-On Natural Language Processing with PyTorch 1.x

By: Thomas Dop

Overview of this book

In the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you’ll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you’ll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You’ll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you’ll learn how to build advanced NLP models, such as conversational chatbots. By the end of this book, you’ll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them.
Table of Contents (14 chapters)
1
Section 1: Essentials of PyTorch 1.x for NLP
7
Section 3: Real-World NLP Applications Using PyTorch 1.x

Installing and using PyTorch 1.x

Like most Python packages, PyTorch is very simple to install. There are two main ways of doing so. The first is to simply install it using pip in the command line. Simply type the following command:

pip install torch torchvision

While this installation method is quick, it is recommended to install using Anaconda instead, as this includes all the required dependencies and binaries for PyTorch to run. Furthermore, Anaconda will be required later to enable training models on a GPU using CUDA. PyTorch can be installed through Anaconda by entering the following in the command line:

conda install torch torchvision -c pytorch

To check that PyTorch is working correctly, we can open a Jupyter Notebook and run a few simple commands:

  1. To define a Tensor in PyTorch, we can do the following:
    import torch
    x = torch.tensor([1.,2.])
    print(x)

    This results in the following output:

    Figure 2.1 – Tensor output

    This shows that tensors within PyTorch...