- BERT stands for Bidirectional Encoder Representation from Transformer. It is a state-of-the-art embedding model published by Google. BERT is a context-based embedding model, unlike other popular embedding models such as word2vec, which are context-free.
- The BERT-base model consists of
, 
, 
, and 110 million parameters, and the BERT-large model consists of 
, 
, 
, and 340 million parameters. - The segment embedding is used to distinguish between the two given sentences. The segment embedding layer returns only either of the two embeddings
and 
as the output. That is, if the input token belongs to sentence A, then the token will be mapped to the embedding 
, and if the token belongs to sentence B, then it will be mapped to the embedding . - BERT is pre-trained using two tasks, namely masked language modeling and next-sentence prediction.
- In the masked language modeling task, in a given input sentence, we randomly mask 15% of the words and train the network...
Getting Started with Google BERT
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Getting Started with Google BERT
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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.
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Current Title:
Getting Started with Google BERT
Table of Contents (15 chapters)
Preface
Section 1 - Starting Off with BERT
Free Chapter
A Primer on Transformers
Understanding the BERT Model
Getting Hands-On with BERT
Section 2 - Exploring BERT Variants
BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT
BERT Variants II - Based on Knowledge Distillation
Section 3 - Applications of BERT
Exploring BERTSUM for Text Summarization
Applying BERT to Other Languages
Exploring Sentence and Domain-Specific BERT
Working with VideoBERT, BART, and More
Assessments
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