In recent years, transformers have emerged as a powerful deep neural network architecture that has been proven to beat the state of the art in many application domains, such as natural language processing (NLP) and computer vision. This post uncovers how you can achieve maximum accuracy with the fastest training time possible when fine-tuning transformers. We demonstrate how the cuML support��
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