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fAsyLex: Accelerating Legal NLP through Comparative Analysis of Multi-GPU Approaches
DescriptionThe primary objective of this work is to conduct an evaluation of the acceleration of NLP training for the task of text classification on legal documents. The dataset used is AsyLex, a dataset of refugee claims from Canada. We implement fast AsyLex (fAsylex) and scale it across up to 64 GPUs. Through systematic experimentation, we seek to address the following research questions: How does the training time differ between single-GPU and multi-GPU setups for two commonly used PLMs? Does the choice of training approach (single-GPU vs. multi-GPU) influence the classification performance on the chosen dataset? We offer an investigation into the practical implications of employing single-GPU and multi-GPU training, we compare two of the most commonly used masked language models, RoBERTa and DeBERTa and reduce runtime out-of-the-box by 49% and 37% respectively, and we demonstrate that there is a trade-off in terms of NLP metrics and distributed training.
Event Type
Workshop
TimeMonday, 13 November 20232:06pm - 2:09pm MST
Location505
State of the Practice
W