transformer architecture
Transformer architecture is a neural network design that models sequence dependencies using self-attention instead of recurrence or convolutions.
A standard transformer stacks encoder and decoder blocks composed of multihead self-attention and positionwise feed-forward layers, wrapped with residual connections and layer normalization.
Transformers can be specialized for different goals, such as encoder-only models for representation and discrimination, decoder-only models for autoregressive generation, and encoder–decoder models for sequence-to-sequence tasks.
Related Resources
Tutorial
Hugging Face Transformers: Leverage Open-Source AI in Python
As the AI boom continues, the Hugging Face platform stands out as the leading open-source model hub. In this tutorial, you'll get hands-on experience with Hugging Face and the Transformers library in Python.
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By Leodanis Pozo Ramos • Updated May 29, 2026
