{"id":13696435,"url":"https://github.com/microsoft/LightLDA","last_synced_at":"2025-05-03T17:31:09.585Z","repository":{"id":55477099,"uuid":"42283396","full_name":"microsoft/LightLDA","owner":"microsoft","description":"Scalable, fast, and lightweight system for large-scale topic modeling","archived":true,"fork":false,"pushed_at":"2020-12-28T12:12:12.000Z","size":84,"stargazers_count":846,"open_issues_count":45,"forks_count":233,"subscribers_count":91,"default_branch":"master","last_synced_at":"2025-04-30T12:47:29.372Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://www.dmtk.io","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/microsoft.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-09-11T02:42:49.000Z","updated_at":"2025-03-15T07:43:39.000Z","dependencies_parsed_at":"2022-08-15T01:10:55.072Z","dependency_job_id":null,"html_url":"https://github.com/microsoft/LightLDA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FLightLDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FLightLDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FLightLDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/microsoft%2FLightLDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/microsoft","download_url":"https://codeload.github.com/microsoft/LightLDA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252226689,"owners_count":21714848,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T18:00:40.074Z","updated_at":"2025-05-03T17:31:09.321Z","avatar_url":"https://github.com/microsoft.png","language":"C++","funding_links":[],"categories":["Models"],"sub_categories":["Latent Dirichlet Allocation (LDA) [:page_facing_up:](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)"],"readme":"# LightLDA\r\n\r\nLightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algorithm, and allows small cluster to tackle very large data and model sizes through model scheduling and data parallelism architecture. LightLDA is implemented with C++ for performance consideration.\r\n\r\nWe have sucessfully trained big topic models (with trillions of parameters) on big data (Top 10% PageRank values of Bing indexed page, containing billions of documents) in Microsoft. For more technical details, please refer to our [WWW'15 paper](http://www.www2015.it/documents/proceedings/proceedings/p1351.pdf). \r\n\r\nFor documents, please view our website [http://www.dmtk.io](http://www.dmtk.io).\r\n\r\n## Why LightLDA\r\n\r\nThe highlight features of LightLDA are\r\n\r\n* **Scalable**: LightLDA can train models with trillions of parameters on big data with billions of documents, a scale previous implementations cann't handle. \r\n* **Fast**: The sampler can sample millions of tokens per second per multi-core node.\r\n* **Lightweight**: Such big tasks can be trained with as few as tens of machines.\r\n\r\n## Quick Start\r\n\r\nRun ``` $ sh build.sh ``` to build lightlda.\r\nRun ``` $ sh example/nytimes.sh ``` for a simple example.\r\n\r\n\r\n## Reference\r\n\r\nPlease cite LightLDA if it helps in your research:\r\n\r\n```\r\n@inproceedings{yuan2015lightlda,\r\n  title={LightLDA: Big Topic Models on Modest Computer Clusters},\r\n  author={Yuan, Jinhui and Gao, Fei and Ho, Qirong and Dai, Wei and Wei, Jinliang and Zheng, Xun and Xing, Eric Po and Liu, Tie-Yan and Ma, Wei-Ying},\r\n  booktitle={Proceedings of the 24th International Conference on World Wide Web},\r\n  pages={1351--1361},\r\n  year={2015},\r\n  organization={International World Wide Web Conferences Steering Committee}\r\n}\r\n```\r\n\r\nMicrosoft Open Source Code of Conduct\r\n------------\r\n\r\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicrosoft%2FLightLDA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmicrosoft%2FLightLDA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmicrosoft%2FLightLDA/lists"}