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Transformers Intro
The architecture that replaced RNNs, starting with 'Attention Is All You Need'.
What is a Transformer?
Introduced in 2017 by Google researchers in the paper "Attention Is All You Need", the Transformer architecture fundamentally changed NLP by replacing sequential processing (RNNs/LSTMs) with parallel processing via Self-Attention.
Level 1 — The Core Concept
The Transformer consists of an Encoder (to understand input) and a Decoder (to generate output). Unlike RNNs that look at words one by one, Transformers look at all words simultaneously.
Key Advantage: Parallelization
Because words are processed in parallel, Transformers can be trained on massive datasets using modern GPUs much faster than previous models.
Level 2 — Architecture Breakdown
A standard Transformer stack includes several identical layers. Each layer has two main sub-layers:
- Multi-Head Self-Attention: Allows the model to focus on different parts of the sentence at once.
- Feed-Forward Neural Network: Processes the information extracted by the attention layer.
Level 3 — Impact on NLP
The Transformer paved the way for "Foundation Models" like BERT and GPT. It solved the problem of "long-range dependencies" where RNNs would forget the beginning of a long sentence by the time they reached the end.
from transformers import pipeline
# The pipeline API is the easiest way to use Transformers
classifier = pipeline("sentiment-analysis")
result = classifier("Transformers are the backbone of modern AI.")
print(result)