Bidirectional Encoder Representations from Transformers (BERT) 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.
The book begins by giving you a detailed explanation of the transformer architecture and helps you understand how the encoder and decoder of the transformer work.
You'll get to grips with BERT and explore its architecture, along with discovering how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it. As you advance, you'll find out about different variants of BERT such as ALBERT, RoBERTa, ELECTRA, and SpanBERT, as well as look into BERT variants based on knowledge distillation, such as DistilBERT and TinyBERT. The book also teaches you about M-BERT, XLM, and XLM-R in detail. You'll then learn about Sentence-BERT, which is used for obtaining sentence representation. You will also see some domain-specific BERT models such as BioBERT and ClinicalBERT. At the end of the book, you will learn about an interesting variant of BERT called VideoBERT.
By the end of this BERT book, you'll be well versed in using BERT and its variants for performing practical NLP tasks.
