attention-is-all-you-need-pytorch

Why we included this project

Students and researchers who want to see the Transformer implementation, rather than consume it through a framework, will appreciate how directly this project mirrors the 2017 'Attention is All You Need' paper. The code lays out self-attention, multi-head attention, positional encoding, and the encoder-decoder stack in plain PyTorch, and ships training scripts that run the WMT'16 German-to-English multimodal task end to end. Because it stays close to the paper's design, tracing exactly where each piece fits is far easier here than inside a larger library. It also makes a tidy starting point for experiments that tweak attention or decoder structure. Just treat it as an academic reference rather than a maintained production tool, and plan to rework the preprocessing for your own data.