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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

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Pages 276 pages
Edition 1st Edition
Languages
Author (1):
Thomas Dop Thomas Dop
Profile icon Thomas Dop

Table of Contents (14) Chapters

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

Exploring state-of-the-art NLP machine learning

While the techniques we have learned in this book so far are highly useful methodologies for training our own machine learning model from scratch, they are far from the most sophisticated models being developed globally. Companies and research groups are constantly striving to create the most advanced machine learning models that will achieve the highest performance on a number of NLP tasks.

Currently, there are two NLP models that have the best performance and could be considered state-of-the-art: BERT and GPT-2. Both models are forms of generalized language models. We will discuss these in more detail in the upcoming sections.

BERT

BERT, which stands for Bidirectional Encoder Representations from Transformers, was developed by Google in 2018 and is widely considered to be the leading model in the field of NLP, having achieved leading performance in natural language inference and question-answering tasks. Fortunately, this has...

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