  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 identifying which
taggers are size-performance optimal. Results indicate that some classical
taggers place on the size-performance skyline across languages. Further,
although the deep models have highest performance for multiple scores, it is
often not the most complex of these that reach peak performance.
