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README.md

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[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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[![Build Status](https://travis-ci.org/cusim.svg?branch=main)](https://travis-ci.org/js1010/cusim)
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[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/dwyl/learn-travis/issues)
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### Introduction
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This project is to speed up various ML models (e.g. topic modeling, word embedding, etc) by CUDA. It would be nice to think of it as [gensim](https://github.com/RaRe-Technologies/gensim)'s GPU version project. As a starting step, I implemented the most widely used word embedding model, the [word2vec](https://arxiv.org/pdf/1301.3781.pdf) model, and the most representative topic model, the [LDA (Latent Dirichlet Allocation)](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) model.

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