  When dealing with the large vocabulary size and corpus size, the run-time for
training Glove model is long, it can even be up to several dozen hours for
data, which is approximately 500MB in size. As a result, finding and selecting
the optimal parameters for the weighting function create many difficulties for
weak hardware. Of course, to get the best results, we need to test benchmarks
many times. In order to solve this problem, we derive a weighting function,
which can save time for choosing parameters and making benchmarks. It also
allows one to obtain nearly similar accuracy at the same given time without
concern for experimentation.
