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

arXiv: https://arxiv.org/abs/2009.04732 · HTML: https://mlsanity.com/paper/2009.04732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot](https://mlsanity.com/paper/2112.15338/) (2021)

## Abstract (arXiv)

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.