Abstract:Improvement in machine learning-based NLP performance are often presented with bigger models and more complex code. This presents a trade-off: better scores come at the cost of larger tools; bigger models tend to require more during training and inference time. We present multiple methods for measuring the size of a model, and for comparing this with the model's performance.In a case study over part-of-speech tagging, we then apply these techniques to taggers for eight languages and present a novel analysis id… Show more
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