Getting Started with Google BERT : Build and train state-of-the-art natural language processing models using BERT

Sudharsan Ravichandiran

ISBN 10: 1838821597 / ISBN 13: 9781838821593

Verlag: Packt Publishing, 2021

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers libraryKey FeaturesExplore the encoder and decoder of the transformer modelBecome well-versed with BERT along with ALBERT, RoBERTa, and DistilBERTDiscover how to pre-train and fine-tune BERT models for several NLP tasksBook 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 LearnUnderstand the transformer model from the ground upFind out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasksGet hands-on with BERT by learning to generate contextual word and sentence embeddingsFine-tune BERT for downstream tasksGet to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT modelsGet the hang of the BERT models based on knowledge distillationUnderstand cross-lingual models such as XLM and XLM-RExplore Sentence-BERT, VideoBERT, and BARTWho 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.

Bestandsnummer des Verkäufers 9781838821593

Bibliografische Details

• Titel: Getting Started with Google BERT : Build and train state-of-the-art natural language processing models using BERT
• Verlag: Packt Publishing
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1838821597
• ISBN-13: 9781838821593
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 20 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 20 mm
• Gewicht: 658 Gramm

Über diesen Titel

Inhaltsangabe

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 Description

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.

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 for

This 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.

Über die Autorin bzw. den Autor

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from 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.