Architecture of BERT

Learn about the different components involved in the BERT transformer in this lesson.

We'll cover the following

Structure
Conclusion

BERT introduces bidirectional attention to transformer models. Bidirectional attention requires many other changes to the original transformer model. In this section, we will focus on the specific aspects of BERT models.

We will focus on the evolutions designed by Devlin et al. (2018), which describe the encoder stack, in the paper "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"The paper can be accessed at: https://arxiv.org/abs/1810.04805.

First we’ll go through the encoder stack the preparation of the pretraining input environment. Then we will describe the two-step framework of BERT: pretraining and fine-tuning.

Let’s first explore the encoder stack.

Structure

The first building block we will take from the original transformer model is an encoder layer. The encoder layer is shown in the diagram below:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Architecture of BERT

Structure