Introduction to the BERT Model

Explore BERT's architecture and its key advantage over traditional models by generating context-based embeddings. Understand how BERT analyzes each word relative to others in a sentence to capture precise meanings, enabling improved performance in various NLP applications.

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Context-based vs. context-free embedding models
- How the context-free embedding model works
- How the context-based embedding model works

BERT stands for Bidirectional Encoder Representation from Transformer. It is the state-of-the-art embedding model published by Google. It has created a major breakthrough in the field of NLP by providing greater results in many NLP tasks, such as question answering, text generation, sentence classification, and many more besides. One of the major reasons for the success of BERT is that it is a context-based embedding model, unlike other popular embedding models, such as word2vec, which are context-free.

1.Before We Start

2.Starting Off with BERT

3.A Primer on Transformers

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4.Understanding the BERT Model

5.Getting Hands-On with BERT

6.Exploring BERT Variants

7.Different BERT Variants

8.BERT Variants—Based on Knowledge Distillation

9.Applications of BERT

10.Exploring BERTSUM for Text Summarization

11.Applying BERT to Other Languages

12.Exploring Sentence and Domain-Specific BERT

13.Working with VideoBERT, BART, and More

14.Conclusion

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Introduction to the BERT Model

Context-based vs. context-free embedding models