- In VideoBERT, along with learning the representation of a language, we also learn the representation of the video. It is the first model to learn the representation of both video and language in a joint manner.
- The VideoBERT model is pre-trained using two important tasks called masked language modeling (cloze task) and the linguistic visual alignment task.
- Similar to the next-sentence prediction task we learned about for BERT, the linguistic visual alignment is also a classification task. But here, we will not predict whether a sentence is the next sentence. Instead, we predict whether the language and the visual tokens are temporally aligned with each other.
- In the text-only method, we mask the language tokens and train the model to predict the masked language tokens. This makes the model better at understanding language representation.
- In the video-only method, we mask the visual tokens and train the model to predict the masked visual...
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
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