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When dealing with the large vocabulary size and corpus size, the run-time fortraining Glove model is long, it can even be up to several dozen hours fordata, which is approximately 500MB in size. As a result, finding and selectingthe optimal parameters for the weighting function create many difficulties forweak hardware. Of course, to get the best results, we need to test benchmarksmany times. In order to solve this problem, we derive a weighting function,which can save time for choosing parameters and making benchmarks. It alsoallows one to obtain nearly similar accuracy at the same given time withoutconcern for experimentation.

Analyze the Effects of Weighting Functions on Cost Function in the Glove Model