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.
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