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Explore the encoder and decoder of the transformer model

Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT

Discover how to pre-train and fine-tune BERT models for several NLP tasks

Description

BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer’s encoder and decoder work. You’ll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you’ll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you’ll be well-versed with using BERT and its variants for performing practical NLP tasks.

What you will learn

Understand the transformer model from the ground up

Find out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasks

Get hands-on with BERT by learning to generate contextual word and sentence embeddings

Fine-tune BERT for downstream tasks

Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models

Get the hang of the BERT models based on knowledge distillation

Understand cross-lingual models such as XLM and XLM-R

Explore Sentence-BERT, VideoBERT, and BART

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Ashwini Mar 05, 2021

I liked this book about BERT. This is one of the great books on BERT that I have come across. The author takes detailed accounts of introduction and applications of BERT before explaining things in detail. I loved the fact that there are frameworks explaining how each and every topic works in BERT. The book is a bit mathy but great for people who want to understand things in details.

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Xan Jan 11, 2022

First of all let me start by saying I am working as an NLP Data Scientist for some years now and my work especially involves using transformers models. Even though people are right about the print of the book the content is invaluable. I really haven’t found another book explaining in such details the transformers models until now. Transformers are the state of the art in NLP so someone should already be comfortable with general NLP to get value out of the book. Kudos to the authors for their writing style which I found entertaining. I hope they will create another version with newest developments and use better printing (maybe partner with o Reilly?). So, for data scientist working currently with transformers this is a very good addition to their library.

Kai Apr 23, 2021

I used this book to learn more about applying Google's BERT in my day-to-day job and Sudharsan does an excellent job to present the topics to the audience in a concise and clear manner, along with quality examples that are easy to follow along.I will not hesitate to recommend this to my peers who are getting into the industry.

Alec Feb 18, 2021

The attention mechanism is hard to learn, the explanatory chapter in this book makes it easier by explaining it showing some vector transformations and matrix multiplication, not just diagrams (as is usual). Nowhere does he say "it works by paying attention", or "it attends over the input", which bolsters my sanity.There are many code examples for all the networks talked about (RoBERTa, DistilBERT, ELECTRA), and a lot of the additional capabilities of BERT are expanded on, like using BERT for question answering. Altogether, it's very comprehensive, and a good source for someone who wants to get started with transformers and wants one source that will tell them what they need to know.

Emanuel Di Nardo Feb 15, 2021

I found this book to be really enjoyable. The author uses a conversational tone and this helps a lot when you try to understand complex neural network models.It begins with an introduction on transformer models: you'll learn about encoders-decoders architectures, seq2seq models with LSTMs & GRUs and all this is wonderfully explained in this book.It feels like the author wrote this book to make people learn and enjoy the topic, rather than just show his math skills as done in some books/papers.The author also introduces several use-cases for Google's BERT architecture: Bert in other languages, VideoBERT, BART and so on.This book is a good one for you if you're looking for a book that helps you understand NLP, BERT architecture with examples and some use-cases in which this kind of model can be applied.

Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT

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