Papers › Neutron: An Implementation of the Transformer Translation Model and its Variants

Neutron: An Implementation of the Transformer Translation Model and its Variants

18 Mar 2019arXiv:1903.07402archive 2025-07-28

Hongfei Xu, Qiuhui Liu

The Transformer translation model is easier to parallelize and provides better performance compared to recurrent seq2seq models, which makes it popular among industry and research community. We implement the Neutron in this work, including the Transformer model and its several variants from most recent researches. It is highly optimized, easy to modify and provides comparable performance with interesting features while keeping readability.

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anoidgit/transformer officialmentioned in papermentioned on GitHubpytorch report

hfxunlp/transformer mentioned on GitHubpytorch report

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Translation

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout LSTM Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Seq2Seq Sigmoid Activation Softmax Tanh Activation Transformer

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