ダウンロード Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT (English Edition) pdf 本

Create cutting-edge, real-world NLP applications with the help of BERT and its variants such as ALBERT, RoBERTa, and DistilBERT Key Features Explore the encoder and decoder of the transformer model in detail Get well-versed with BERT along with its variants such as ALBERT, RoBERTa, and DistilBERT Discover how to pre-train and fine-tune BERT models for modern NLP tasks Book Description Bidirectional Encoder Representations from Transformers (BERT) 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. The book begins by giving you a detailed explanation of the transformer architecture and helping you understand how the encoder and decoder of the transformer work. You’ll get to grips with BERT and explore its architecture, along with discovering how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it. As you advance, you’ll find out about different variants of BERT such as ALBERT, RoBERTa, ELECTRA, and SpanBERT, as well as look into BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book also teaches you about MBERT, XLM, and XLM-R in detail using practical examples. You’ll then learn about sentence-BERT which is used for obtaining sentence representation, and look into some domain-specific BERT models such as BioBERT and ClinicalBERT. Throughout the book, you’ll work with various examples to cover text classification and summarization, language representation, captioning, and question answering models. By the end of this BERT book, you’ll be well versed in 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 how to pre-train and use it Implement BERT for text classification and QA systems Perform named entity recognition and text summarization tasks using BERT Discover transfer learning and learn how to tune the BERT model for several downstream tasks Get to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT models Delve into the BERT models based on knowledge distillation Explore cross-lingual models such as XLM and XLM-R and obtain sentence embeddings with SBERT 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. Basic understanding of NLP concepts and deep learning is required to get the best out of this book.