Training and performance
The original Transformer was trained on a 4.5-million-sentence-pair English-German dataset and a 36-million-sentence English-French dataset.
The datasets come from Workshops on Machine Translation (WMT), which can be found at the following link if you wish to explore the WMT datasets: http://www.statmt.org/wmt14/
The training of the original Transformer base models took 12 hours to train for 100,000 steps on a machine with 8 NVIDIA P100 GPUs. The big models took 3.5 days for 300,000 steps.
The original Transformer outperformed all the previous machine translation models with a BLEU score of 41.8. The result was obtained on the WMT English-to-French dataset.
BLEU stands for Bilingual Evaluation Understudy. It is an algorithm that evaluates the quality of the results of machine translations.
The Google Research and Google Brain team applied optimization strategies to improve the performance of the Transformer. For example, the Adam optimizer...
