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Natural Language Processing with PyTorch 1st Edition (2019).pdf

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    Preface
    Conventions Used in This Book
    Using Code Examples
    O’Reilly Safari
    How to Contact Us
    Acknowledments
    1. Introduction
    The Supervised Learning Paradigm
    Observation and Target Encoding
    One-Hot Representation
    TF Representation
    TF-IDF Representation
    Target Encoding
    Computational Graphs
    PyTorch Basics
    Installing PyTorch
    Creating Tensors
    Tensor Types and Size
    Tensor Operations
    Indexing, Slicing, and Joining
    Tensors and Computational Graphs
    CUDA Tensors
    Exercises
    Solutions
    Summary
    References
    2. A Quick Tour of Traditional NLP
    Corpora, Tokens, and Types
    Unigrams, Bigrams, Trigrams, …, N-grams
    Lemmas and Stems
    Categorizing Sentences and Documents
    Categorizing Words: POS Tagging
    Categorizing Spans: Chunking and Named Entity Recognition
    Structure of Sentences
    Word Senses and Semantics
    Summary
    References
    3. Foundational Components of Neural Networks
    The Perceptron: The Simplest Neural Network
    Activation Functions
    Sigmoid
    Tanh
    ReLU
    Softmax
    Loss Functions
    Mean Squared Error Loss
    Categorical Cross-Entropy Loss
    Binary Cross-Entropy Loss
    Diving Deep into Supervised Training
    Constructing Toy Data
    Putting It Together: Gradient-Based Supervised Learning
    Auxiliary Training Concepts
    Correctly Measuring Model Performance: Evaluation Metrics
    Correctly Measuring Model Performance: Splitting the Dataset
    Knowing When to Stop Training
    Finding the Right Hyperparameters
    Regularization
    Example: Classifying Sentiment of Restaurant Reviews
    The Yelp Review Dataset
    Understanding PyTorch’s Dataset Representation
    The Vocabulary, the Vectorizer, and the DataLoader
    A Perceptron Classifier
    The Training Routine
    Evaluation, Inference, and Inspection
    Summary
    References
    4. Feed-Forward Networks for Natural Language Processing
    The Multilayer Perceptron
    A Simple Example: XOR
    Implementing MLPs in PyTorch
    Example: Surname Classification with an MLP
    The Surnames Dataset
    Vocabulary, Vectorizer, and DataLoader
    The SurnameClassifier Model
    The Training Routine
    Model Evaluation and Prediction
    Regularizing MLPs: Weight Regularization and Structural Regularization (or Dropout)
    Convolutional Neural Networks
    CNN Hyperparameters
    Implementing CNNs in PyTorch
    Example: Classifying Surnames by Using a CNN
    The SurnameDataset Class
    Vocabulary, Vectorizer, and DataLoader
    Reimplementing the SurnameClassifier with Convolutional Networks
    The Training Routine
    Model Evaluation and Prediction
    Miscellaneous Topics in CNNs
    Pooling
    Batch Normalization (BatchNorm)
    Network-in-Network Connections (1x1 Convolutions)
    Residual Connections/Residual Block
    Summary
    References
    5. Embedding Words and Types
    Why Learn Embeddings?
    Efficiency of Embeddings
    Approaches to Learning Word Embeddings
    The Practical Use of Pretrained Word Embeddings
    Example: Learning the Continuous Bag of Words Embeddings
    The Frankenstein Dataset
    Vocabulary, Vectorizer, and DataLoader
    The CBOWClassifier Model
    The Training Routine
    Model Evaluation and Prediction
    Example: Transfer Learning Using Pretrained Embeddings for Document Classification
    The AG News Dataset
    Vocabulary, Vectorizer, and DataLoader
    The NewsClassifier Model
    The Training Routine
    Model Evaluation and Prediction
    Evaluating on the test dataset
    Summary
    References
    6. Sequence Modeling for Natural Language Processing
    Introduction to Recurrent Neural Networks
    Implementing an Elman RNN
    Example: Classifying Surname Nationality Using a Character RNN
    The SurnameDataset Class
    The Vectorization Data Structures
    The SurnameClassifier Model
    The Training Routine and Results
    Summary
    References
    7. Intermediate Sequence Modeling for Natural Language Processing
    The Problem with Vanilla RNNs (or Elman RNNs)
    Gating as a Solution to a Vanilla RNN’s Challenges
    Example: A Character RNN for Generating Surnames
    The SurnameDataset Class
    The Vectorization Data Structures
    From the ElmanRNN to the GRU
    Model 1: The Unconditioned SurnameGenerationModel
    Model 2: The Conditioned SurnameGenerationModel
    The Training Routine and Results
    Tips and Tricks for Training Sequence Models
    References
    8. Advanced Sequence Modeling for Natural Language Processing
    Sequence-to-Sequence Models, Encoder–Decoder Models, and Conditioned Generation
    Capturing More from a Sequence: Bidirectional Recurrent Models
    Capturing More from a Sequence: Attention
    Attention in Deep Neural Networks
    Evaluating Sequence Generation Models
    Example: Neural Machine Translation
    The Machine Translation Dataset
    A Vectorization Pipeline for NMT
    Encoding and Decoding in the NMT Model
    The Training Routine and Results
    Summary
    References
    9. Classics, Frontiers, and Next Steps
    What Have We Learned so Far?
    Timeless Topics in NLP
    Dialogue and Interactive Systems
    Discourse
    Information Extraction and Text Mining
    Document Analysis and Retrieval
    Frontiers in NLP
    Design Patterns for Production NLP Systems
    Where Next?
    References
    Index
    Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning Delip Rao and Brian McMahan
    Natural Language Processing with PyTorch by Delip Rao and Brian McMahan Copyright © 2019 Delip Rao and Brian McMahan. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800- 998-9938 or corporate@oreilly.com. Acquisition Editor: Rachel Roumeliotis Development Editor: Jeff Bleiel Production Editor: Nan Barber Copyeditor: Octal Publishing, LLC Proofreader: Rachel Head Indexer: Judy McConville Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest February 2019: First Edition Revision History for the First Edition 2019-01-16: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781491978238 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Natural Language Processing with PyTorch, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or
    omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-97823-8 [LSI]
    Preface This book aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground. While writing the book, we had to make difficult, and sometimes uncomfortable, choices on what material to leave out. For a beginner reader, we hope the book will provide a strong foundation in the basics and a glimpse of what is possible. Machine learning, and deep learning in particular, is an experiential discipline, as opposed to an intellectual science. The generous end-to-end code examples in each chapter invite you to partake in that experience. When we began working on the book, we started with PyTorch 0.2. The examples were revised with each PyTorch update from 0.2 to 0.4. PyTorch 1.0 is due to release around when this book comes out. The code examples in the book are PyTorch 0.4–compliant and should work as they are with the upcoming PyTorch 1.0 release. A note regarding the style of the book. We have intentionally avoided mathematics in most places, not because deep learning math is particularly difficult (it is not), but because it is a distraction in many situations from the main goal of this book—to empower the beginner learner. Likewise, in many cases, both in code and text, we have favored exposition over succinctness. Advanced readers and experienced programmers will likely see ways to tighten up the code and so on, but our choice was to be as explicit as possible so as to reach the broadest of the audience that we want to reach. 1 Conventions Used in This Book The following typographical conventions are used in this book: Italic
    Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values determined by context. This element signifies a tip or suggestion. TIP NOTE This element signifies a general note. This element indicates a warning or caution. WARNING Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://nlproc.info/PyTorchNLPBook/repo/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant
    portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD- ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Natural Language Processing with PyTorch by Delip Rao and Brian McMahan (O’Reilly). Copyright 2019, Delip Rao and Brian McMahan, 978-1-491-97823-8.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com. O’Reilly Safari Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals. Members have access to thousands of books, training videos, Learning Paths, interactive tutorials, and curated playlists from over 250 publishers, including O’Reilly Media, Harvard Business Review, Prentice Hall Professional, Addison- Wesley Professional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others. For more information, please visit http://oreilly.com/safari. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North
    Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/nlprocbk. To comment or ask technical questions about this book, send email to bookquestions@oreilly.com. For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com. Find us on Facebook: http://facebook.com/oreilly Follow us on Twitter: http://twitter.com/oreillymedia Watch us on YouTube: http://www.youtube.com/oreillymedia Acknowledments This book has gone through an evolution of sorts, with each version of the book looking unlike the version before. Different folks (and even different DL frameworks) were involved in each version. The authors want to thank Goku Mohandas for his initial involvement in the book. Goku brought a lot of energy to the project before he had to leave for work reasons. Goku’s enthusiasm for PyTorch and his positivity are unmatched, and the authors missed his presence. We expect great things coming from him! The book would not be in top technical form if it not for the kind yet high- quality feedback from our technical reviewers, Liling Tan and Debasish Gosh. Liling contributed his expertise in developing products with state-of-the-art NLP, while Debasish gave highly valuable feedback from the perspective of the developer audience. We are also grateful for the encouragement from Alfredo Canziani, Soumith Chintala, and the many other amazing folks on the PyTorch
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