Understanding BERT for Beginners

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Introduction

BERT (Bidirectional Encoder Representations from Transformers) is a powerful language representation model. It understands context in language by considering both the left and right context of each word in a sentence.

1. Tokenization

In natural language processing, we start with breaking down a sentence into smaller units, called tokens. Think of tokens as the building blocks of a sentence, like individual words.

2. Embeddings

BERT uses embeddings to represent words in a way that the model can understand. Embeddings are like numerical representations of words that capture their meaning.

a. Token Embedding

Each word (or token) gets a unique numerical representation. This helps BERT understand the meaning of each word.

b. Segment Embedding

BERT can understand different segments within a sentence. For example, in a question-answering task, there are two segments: the question and the answer.

c. Position Embedding

The order of words matters. Position embeddings help BERT understand where each word is located in a sentence.

3. BERT Model

Now, let's talk about the main part - the BERT model itself.

a. Transformer Encoder

BERT uses a Transformer, a type of neural network architecture. The Transformer processes information in a way that captures the relationships between words in a sentence.

b. Stacking Layers

BERT doesn't use just one Transformer layer; it stacks multiple layers. Each layer refines the understanding of language, making the model more powerful.

Putting It All Together

Now, let's see how these pieces fit together using simple analogies:

• Tokenization: Imagine a sentence as a puzzle. Tokenization breaks the sentence into puzzle pieces (tokens).

• Embeddings: Think of embeddings as labels on each puzzle piece, helping BERT understand the words, their order, and the context.

• Transformer Encoder: Picture a smart friend who arranges these puzzle pieces in a way that reveals the bigger picture – that's the Transformer. Stacking multiple layers (like having multiple smart friends) refines the understanding.

Sample Use Case

Let's say you want to teach a computer to understand a simple story:

Story:

John went to the store. He bought some apples and paid at the counter.

Tokenization:

• Tokens: [John, went, to, the, store, ., He, bought, some, apples, and, paid, at, the, counter, .]

Embeddings:

• Token Embedding: Numbers for each word.
• Segment Embedding: Labels for different parts (John's action and the store action).
• Position Embedding: Numbers representing the order of words.

BERT Model:

• The BERT model processes these embeddings through multiple layers, refining its understanding of the story.

 

 

If you're interested in diving deeper and getting hands-on experience with BERT implementation from scratch in PyTorch, check out this notebook on GitHub. This notebook walks you through the step-by-step process of building a simple BERT model using PyTorch, providing a hands-on approach to understanding the inner workings of BERT. Feel free to explore and experiment with the code to enhance your understanding of BERT architecture and its implementation.