Getting Started with Google BERT
Build and train state-of-the-art natural language processing models using BERT
Seiten
2021
Packt Publishing Limited (Verlag)
978-1-83882-159-3 (ISBN)
Packt Publishing Limited (Verlag)
978-1-83882-159-3 (ISBN)
Getting Started with Google BERT will help you become well-versed with the BERT model from scratch and learn how to create interesting NLP applications. You'll understand several variants of BERT such as ALBERT, RoBERTa, DistilBERT, ELECTRA, VideoBERT, and many others in detail.
Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library
Key Features
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.
Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library
Key Features
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.
Sudharsan Ravichandiran is a data scientist, researcher, bestselling author. He completed his Bachelor's in Information Technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, including Natural Language Processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.
Table of Contents
A Primer on Transformer Model
Understanding the BERT Model
Getting Hands-On with BERT
BERT variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT
BERT variants II - Based on knowledge distillation
Exploring BERTSUM for Text Summarization
Applying BERT for Other Languages
Exploring Sentence and Domain Specific BERT
Working with VideoBERT, BART, and more
| Erscheinungsdatum | 27.01.2021 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-83882-159-7 / 1838821597 |
| ISBN-13 | 978-1-83882-159-3 / 9781838821593 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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