{"id":13696860,"url":"https://github.com/lucastheis/logistic_lda","last_synced_at":"2025-05-03T17:32:24.418Z","repository":{"id":141344406,"uuid":"194238820","full_name":"lucastheis/logistic_lda","owner":"lucastheis","description":"Basic tensorflow implementation of logistic latent Dirichlet allocation","archived":false,"fork":false,"pushed_at":"2019-10-17T16:46:40.000Z","size":50,"stargazers_count":18,"open_issues_count":0,"forks_count":5,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-11-13T00:33:10.515Z","etag":null,"topics":["classification","lda","machine-learning","tensorflow","topic-modeling"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lucastheis.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2019-06-28T08:40:14.000Z","updated_at":"2023-12-22T18:51:13.000Z","dependencies_parsed_at":"2023-05-04T08:02:03.574Z","dependency_job_id":null,"html_url":"https://github.com/lucastheis/logistic_lda","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/lucastheis%2Flogistic_lda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucastheis%2Flogistic_lda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucastheis%2Flogistic_lda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucastheis%2Flogistic_lda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lucastheis","download_url":"https://codeload.github.com/lucastheis/logistic_lda/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252226856,"owners_count":21714884,"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":["classification","lda","machine-learning","tensorflow","topic-modeling"],"created_at":"2024-08-02T18:00:48.691Z","updated_at":"2025-05-03T17:32:24.097Z","avatar_url":"https://github.com/lucastheis.png","language":"Python","funding_links":[],"categories":["Models"],"sub_categories":["Miscellaneous topic models"],"readme":"Logistic LDA\n============\n\nThis package provides basic implementations of _logistic latent Dirichlet allocation_. It can be\nused to discover topics in data containing groups of thematically related items, using either\nlabeled data or unlabeled data.\n\nIf you want to reproduce experiments of our paper, start here instead instead: [:octocat: logistic-lda/experiments](https://github.com/lucastheis/logistic_lda/tree/experiments)\n\nRequirements\n------------\n\n* tensorflow == 1.13.2\n* numpy \u003e= 1.16.4\n\nThe code was tested with the versions above, but older versions might also work.\n\n\nGetting started\n---------------\n\nTo get started, download a version of the 20-Newsgroups dataset in TFRecord format:\n\n\t./scripts/download_news20.sh\n\nOnce downloaded, training can be started with:\n\n\t./scripts/train_news20.sh\n\nTo use your own dataset, take a look at `./logistic_lda/data.py` for a description of the data\nformat expected by the training script. Alternatively, modify the training script to use datasets\nnot stored as TFRecords.\n\nAfter training has finished, compute predictions on another dataset and evaluate accuracy:\n\n\t./scripts/evaluate_news20.sh\n\nThe results of the evaluation can be found in `./models/news20/`.\n\n\nReference\n---------\n\nI. Korshunova, H. Xiong, M. Fedoryszak, L. Theis  \n*Discriminative Topic Modeling with Logistic LDA*  \nAdvances in Neural Information Processing Systems 33, 2019\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucastheis%2Flogistic_lda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucastheis%2Flogistic_lda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucastheis%2Flogistic_lda/lists"}