{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/familia-an-open-source-toolkit-for-industrial","title":"Familia: An Open-Source Toolkit for Industrial Topic Modeling","arxiv_id":"1707.09823","date":"2017-07-31","proceeding":null,"authors":["Di Jiang","Zeyu Chen","Rongzhong Lian","Siqi Bao","Chen Li"],"abstract":"Familia is an open-source toolkit for pragmatic topic modeling in industry.\nFamilia abstracts the utilities of topic modeling in industry as two paradigms:\nsemantic representation and semantic matching. Efficient implementations of the\ntwo paradigms are made publicly available for the first time. Furthermore, we\nprovide off-the-shelf topic models trained on large-scale industrial corpora,\nincluding Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word\nEmbedding (TWE). We further describe typical applications which are\nsuccessfully powered by topic modeling, in order to ease the confusions and\ndifficulties of software engineers during topic model selection and\nutilization.","url_abs":"http://arxiv.org/abs/1707.09823v1","url_pdf":"http://arxiv.org/pdf/1707.09823v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"familia-an-open-source-toolkit-for-industrial","repo_url":"https://github.com/baidu/Familia","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}