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

Introducing TinyBERT

TinyBERT is another interesting variant of BERT that also uses knowledge distillation. With DistilBERT, we learned how to transfer knowledge from the output layer of the teacher BERT to the student BERT. But apart from this, can we also transfer knowledge from the other layers of the teacher BERT? Yes! Apart from transferring knowledge from the output layer of the teacher to the student BERT, we can also transfer knowledge from other layers.

In TinyBERT, apart from transferring knowledge from the output layer (prediction layer) of the teacher to the student, we also transfer knowledge from embedding and encoder layers.

Let's understand this with an example. Suppose we have a teacher BERT with N encoder layers. For simplicity, we have shown only one encoder layer in the following figure. The following figure depicts the pre-trained teacher BERT model where we feed a masked sentence and it returns the logits of all the words in our vocabulary being the masked...