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Getting Started with Google BERT

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Before We Start

About the course

Starting Off with BERT

Introduction: BERT

A Primer on Transformers

Introduction to the Transformer Understanding the Encoder of the Transformer Self-Attention Mechanism Understanding the Self-Attention Mechanism Multi-Head Attention Mechanism Learning Position with Positional Encoding Encoder: Feedforward and Add and Norm Component Putting All the Encoder Components Together Understanding the Decoder of the Transformer Masked Multi-Head Attention Multi-Head Attention Decoder: Feedforward and Add and Norm Component Putting All the Decoder Components Together Putting the Encoder and Decoder Together Summary: A Primer on Transformers Exercise: A Primer on Transformers Exercise: Decoder Architecture Quiz: A Primer on Transformers

Understanding the BERT Model

Introduction to the BERT Model Working of BERT Configurations of BERT Pre-Training the BERT Model The WordPiece Tokenizer Pre-Training Strategies for the BERT Model Masked Language Modeling Next Sentence Prediction Pre-Training Procedure Subword Tokenization Algorithms Byte Pair Encoding Byte-Level Byte Pair Encoding and WordPiece Algorithms Summary: Understanding the BERT Model Quiz: Understanding the BERT Model

Getting Hands-On with BERT

Exploring the Pre-Trained BERT Model

Extracting Embeddings From All Encoder Layers of BERT

Fine-Tuning BERT for Downstream Tasks

Sentiment Analysis

Natural Language Interface

Question-Answering

Performing Question-Answering with the Fine-Tuned BERT

Named Entity Recognition

Summary: Getting Hands-On with BERT

Quiz: Getting Hands-On with BERT

Exploring BERT Variants

Introducion: BERT Variants

Different BERT Variants

ALBERT ALBERT : Training the Model ALBERT : Embeddings Extraction RoBERTa RoBERTa Tokenizer ELECTRA Generator and Discriminator of the ELECTRA Model Training the ELECTRA Model SpanBERT SpanBERT: Exploring Architecture Summary: Different BERT Variants Quiz: Different BERT Variants

BERT Variants—Based on Knowledge Distillation

Knowledge Distillation Training the Student Network DistilBERT Training the Student BERT (DistilBERT)TinyBERT Teacher-Student Architecture Distillation of Transformer Layer Distillation of Embedding and Prediction Layer Distillation Techniques for Pre-training and Fine-tuning Data Augmentation Transferring Knowledge from BERT to Neural Networks The Data Augmentation Methods Summary: BERT Variants—Based on Knowledge Distillation Quiz: BERT Variants—Based on Knowledge Distillation

Applications of BERT

Introduction: Applications of BERT

Exploring BERTSUM for Text Summarization

Text Summarization Fine-Tuning BERT for Extractive Summarization BERTSUM for Extractive Summary BERTSUM for Abstractive Summarization ROUGE Evaluation Metrics Training the BERTSUM Model Summary: Exploring BERTSUM for Text Summarization Quiz: Exploring BERTSUM for Text Summarization

Applying BERT to Other Languages

Understanding Multilingual BERT Evaluating M-BERT on the NLI task How Multilingual is Multilingual BERT?M-BERT Generalization Effect of Code-Switching and Transliteration The Cross-Lingual Language Model (XLM)The XLM-R Model Language-Specific BERT BETO for Spanish BERTje for Dutch German BERT Chinese BERT Japanese BERT FinBERT for Finnish BERT Models for Italian and Portuguese RuBERT for Russian Summary: Applying BERT to Other Languages Quiz: Applying BERT to Other Languages

Exploring Sentence and Domain-Specific BERT

Sentence-BERT Sentence-BERT with a Siamese Network Sentence-BERT with a Triplet Network Use Cases of Sentence-BERT Model Learning Multilingual Embeddings Through Knowledge Distillation Multilingual Sentence-BERT Model Domain-Specific BERT BioBERT Summary: Exploring Sentence and Domain-Specific BERT Quiz: Exploring Sentence and Domain-Specific BERT

Working with VideoBERT, BART, and More

VideoBERT Model Pre-training Dataset and Applications of VideoBERT BART Model Noising Techniques Performing Text Summarization with BART Exploring BERT Libraries Building a Document Answering Model Document Summarization Summary: Working with VideoBERT, BART, and More Quiz: Working with VideoBERT, BART, and More

Conclusion

What We've Learned

Quiz: Getting Hands-On with BERT

Take a short quiz to test your understanding of the BERT model.

1

What is the main difference between BERT-cased and BERT-uncased models?

A)

The tokens are lowercased in BERT-uncased but not in BERT-cased.

B)

BERT-cased uses a different pre-processing method than BERT-uncased.

C)

BERT-uncased is trained on a larger dataset than BERT-cased.

D)

BERT-uncased is more suitable for NER tasks than BERT-cased.

Question 1 of 100 attempted

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