Implementation of the Transformer model from the paper Attention Is All You Need in Pytorch.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. This paper propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. This model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, this model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. This paper show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
- implement the transformer model for machine translation.
- implement the encoder and decoder.
- implement the positional encoding.
- implement the multi-head attention.
- implement the feed forward network.
- implement the training and evaluation code.
@misc{Vaswani2017,
author = {Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., & Polosukhin I.},
title = {Attention Is All You Need},
year = {2017},
eprint = {arXiv:1706.03762},
}