Transformers in NLP

In earlier lessons, we studied RNNs, LSTMs, and GRUs, which process text sequentially. While these models improved sequence modeling, they still struggled with long dependencies and slow training. Transformers were introduced to overcome these limitations and revolutionized Natural Language Processing.

This lesson explains what transformers are, why they replaced RNN-based models, and how they work at a high level.

Real-World Connection

Modern systems like ChatGPT, Google Translate, recommendation engines, and document search platforms are all powered by transformer-based models. These systems understand context, long documents, and complex sentence structures far better than older models.

If an AI system understands meaning across paragraphs instead of just nearby words, transformers are the reason.

What Is a Transformer?

A transformer is a neural network architecture designed to process entire sequences at once instead of word by word. It uses a mechanism called attention to understand relationships between all words in a sentence simultaneously.

• Processes tokens in parallel
• Captures long-range dependencies
• Trains much faster than RNNs

Why Transformers Replaced RNNs

RNN-based models process text sequentially, which limits speed and memory. Transformers remove this dependency by allowing every word to attend to every other word.

• No sequential bottleneck
• Better context understanding
• Scales well to large datasets

The Attention Mechanism

Attention allows a model to focus on the most relevant words when processing a sentence. Instead of treating all words equally, attention assigns higher importance to words that matter more for understanding meaning.

For example, in the sentence “The animal didn’t cross the road because it was tired,” attention helps the model understand what “it” refers to.

High-Level Transformer Structure

• Input embeddings
• Positional encoding
• Multi-head self-attention
• Feed-forward neural networks
• Layer normalization and residual connections

Simple Transformer Usage Example

Below is a minimal example showing how a transformer-based model is loaded and used for text understanding.

from transformers import pipeline

classifier = pipeline("sentiment-analysis")

result = classifier("Transformers are changing NLP forever")
print(result)
  

Understanding the Code

The pipeline automatically loads a pretrained transformer model. The input sentence is tokenized, processed using attention layers, and classified based on learned representations.

The output label and confidence score indicate how strongly the model predicts the sentiment.

Encoder vs Decoder Transformers

Transformers can be divided into encoder-based, decoder-based, and encoder-decoder models.

• Encoder-only models focus on understanding text
• Decoder-only models generate text
• Encoder-decoder models transform text from one form to another

Popular Transformer Models

• BERT – text understanding
• GPT – text generation
• T5 – text-to-text tasks
• RoBERTa – optimized BERT variant

Where Transformers Are Used

• Chatbots and virtual assistants
• Machine translation
• Search engines
• Text summarization
• Question answering

Practice Questions

Quick Quiz

Coming up next: BERT Basics — understanding encoder-only transformer models.