# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "sbert" in publications use:' type: software license: MIT title: 'sbert: Topic Modeling with ''Sentence-BERT'' Embeddings' version: 0.5.8 doi: 10.32614/CRAN.package.sbert abstract: Builds semantic topic models from 'Sentence-BERT' embeddings (Reimers and Gurevych, 2019) . Sentence embeddings are computed in several modes with models run on the Open Neural Network Exchange ('ONNX') Runtime, including multilingual and long-context encoders and a fast static model. Topics are labeled with class-based term weighting that can be retuned without refitting. Candidate topic counts and document granularities (whole documents, sentences, clauses, or phrases) can be compared side by side, and fitted models assign topics to new documents, give soft memberships and per-document topic mixtures. The package offers coherence and diversity metrics and visualizations. authors: - family-names: Saqr given-names: Mohammed email: mohammed.saqr@uef.fi orcid: https://orcid.org/0000-0001-5881-3109 - family-names: López-Pernas given-names: Sonsoles email: sonsoles.lopez@uef.fi orcid: https://orcid.org/0000-0002-9621-1392 repository: https://mohsaqr.r-universe.dev repository-code: https://github.com/sonsoleslp/sbert commit: 387023d12ebc6cc73b8a32332fcc60c56ad79af7 url: https://sonsoles.me/sbert/ date-released: '2026-10-09' contact: - family-names: López-Pernas given-names: Sonsoles email: sonsoles.lopez@uef.fi orcid: https://orcid.org/0000-0002-9621-1392