- eBook:Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT
- Author:Sudharsan Ravichandiran
- Data:January 22, 2021
- Pages:352 pages
- Format:PDF, ePUB
- Explore the encoder and decoder of the transformer model
- Become well-versed with BERT along with ALBERT, RoBERTa, and DistilBERT
- Discover how to pre-train and fine-tune BERT models for several NLP tasks
Book DescriptionBERT (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.
What you will learn
- 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
Who this book is forThis book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.
Chapter 1: A Primer on Transformers
Chapter 2: Understanding the BERT Model
Chapter 3: Getting Hands-On with BERT
Section 2 - Exploring BERT Variants
Chapter 4: BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT
Chapter 5: BERT Variants II - Based on Knowledge Distillation
Section 3 - Applications of BERT
Chapter 6: Exploring BERTSUM for Text Summarization
Chapter 7: Applying BERT to Other Languages
Chapter 8: Exploring Sentence and Domain-Specific BERT
Chapter 9: Working with VideoBERT, BART, and More