Book Image

Getting Started with Google BERT

By : Sudharsan Ravichandiran
Book Image

Getting Started with Google BERT

By: Sudharsan Ravichandiran

Overview of this book

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.
Table of Contents (15 chapters)
1
Section 1 - Starting Off with BERT
5
Section 2 - Exploring BERT Variants
8
Section 3 - Applications of BERT
Applying BERT to Other Languages

In previous chapters, we learned how BERT works and we also explored its different variants. Hitherto, however, we have only applied BERT to the English language. Can we also apply BERT to other languages? The answer to this question is yes, and that's precisely what we will learn in this chapter. We will use multilingual BERT (M-BERT) to compute the representation of different languages other than English. We will begin the chapter by understanding how M-BERT works and how to use it.

Next, we will understand how multilingual the M-BERT model is by investigating it in detail. Following this, we will learn about the XLM model. XLM stands for the cross-lingual language model, which is used to obtain cross-lingual representations. We will understand how XLM works and how it differs from M-BERT in detail.

Following on from this, we will learn...