Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Getting Started with Google BERT

You're reading from Getting Started with Google BERT Build and train state-of-the-art natural language processing models using BERT

Product type Paperback

Published in Jan 2021

Publisher Packt

ISBN-13 9781838821593

Length 352 pages

Edition 1st Edition

Languages

Python

Tools

BERT

Concepts

Mobile Application Development

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1 - Starting Off with BERT

2. A Primer on Transformers FREE CHAPTER

3. Understanding the BERT Model

4. Getting Hands-On with BERT

5. Section 2 - Exploring BERT Variants

6. BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT

7. BERT Variants II - Based on Knowledge Distillation

8. Section 3 - Applications of BERT

9. Exploring BERTSUM for Text Summarization

10. Applying BERT to Other Languages

11. Exploring Sentence and Domain-Specific BERT

12. Working with VideoBERT, BART, and More

13. Assessments

14. Other Books You May Enjoy

Leave a review - let other readers know what you think

Text summarization

Text summarization is the process of converting a long text into its summary. Suppose we have a Wikipedia article and say we don't want to read the whole article – we just need an overview of the article. In this case, summarizing the Wikipedia article will help us get an overview of the article. Text summarization is widely used for a variety of applications, from summarizing long documents, news articles, blog posts, ranging to many more. In the text summarization task, given a long text, our goal is to convert the given long text into its summary as shown in the figure:

Figure 6.1 – Text summarization

Text summarization is of two types:

Extractive summarization
Abstractive summarization

Now let's explore extractive and abstractive summarization in detail.

Extractive summarization

In extractive summarization, we create a summary from a given text by extracting only the important sentences.

That is, say we are given a long document containing...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

See other products by Sudharsan Ravichandiran

Other recommended products

Related to this chapter

Mastering Transformers

Mastering Transformers

Explore the accurate and fast fine-tuning capabilities of transformer-based language models and understand how they outperform traditional machine learning-based approaches when solving challenging NLU problems. Developers working with the Transformers architecture will be able to put their knowledge to work with this practical guide.

Sep 2021 12h 28m

Transformers for Natural Language Processing

Transformers for Natural Language Processing

Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP.

Jan 2021 12h 48m

Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

Jul 2019 17h 4m

Advanced Natural Language Processing with TensorFlow 2

Advanced Natural Language Processing with TensorFlow 2

This book provides hands-on training in NLP tools and techniques with intrinsic details. Apart from gaining expertise, you will be able to carry out novel state-of-the-art research using the skills gained.

Feb 2021 12h 40m

Hands-On Natural Language Processing with PyTorch 1.x

Hands-On Natural Language Processing with PyTorch 1.x

Developers working with NLP will be able to put their knowledge to work with this practical guide to PyTorch. You will learn to use PyTorch offerings and how to understand and analyze text using Python. You will learn to extract the underlying meaning in the text using deep neural networks and modern deep learning algorithms.

Jul 2020 9h 12m

Mastering spaCy

Mastering spaCy

Using machine learning-based NLP models, you can speed up business processes, make more accurate predictions, and uncover new insights from your existing data, where spaCy, an advanced industrial-grade natural language processing library, can help. With this book, you'll learn how to use it and create high-impact ML solutions for NLP.

Jul 2021 11h 52m

Hands-On Python Natural Language Processing

Hands-On Python Natural Language Processing

This book provides a blend of both the theoretical and practical aspects of Natural Language Processing (NLP). It covers the concepts essential to develop a thorough understanding of NLP and also delves into a detailed discussion on NLP based use-cases such as language translation, sentiment analysis, etc. Every module covers real-world examples

Jun 2020 10h 32m

Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today's vast amount of unstructured data.

May 2018 15h 44m

Advanced Deep Learning with Python

Advanced Deep Learning with Python

This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.

Dec 2019 15h 36m

Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML.

Dec 2019 21h 32m