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Training Tips for the Transformer Model
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Training Tips for the Transformer Model
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Training Tips for the Transformer Model
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Journal Article
Training Tips for the Transformer Model
2018
Overview
This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (
). We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers. In addition to confirming the general mantra “more data and larger models”, we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging. We hope that our observations will allow others to get better results given their particular hardware and data constraints.
Publisher
De Gruyter Open,Institute of Formal and Applied Linguistics
Subject
