Getting Started with Transformers
A practical guide to understanding transformer architecture and using it in modern NLP workflows.
2/14/2026-1 min read-Machine Learning-Mubin Ahmed
Transformers are now the default architecture for most state-of-the-art natural language systems.
Why transformers changed everything
The key shift was replacing recurrence with attention, allowing models to process tokens in parallel and learn long-range relationships.
Core attention intuition
Self-attention lets each token weigh every other token before producing its representation.
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Practical usage tips
Start with proven open-source models, benchmark on your own dataset, and optimize inference cost early.