sbert: Topic Modeling with 'Sentence-BERT' Embeddings
Builds semantic topic models from 'Sentence-BERT'
embeddings (Reimers and Gurevych, 2019) <doi:10.18653/v1/D19-1410>.
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.
| Version: |
0.5.8 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
digest, grDevices, graphics, onnxr (≥ 0.1.2), parallel, tok (≥ 0.2.3), stats, tools, utils |
| Suggests: |
SnowballC, jsonlite, knitr, rmarkdown, testthat (≥ 3.0.0), withr |
| Published: |
2026-10-21 |
| DOI: |
10.32614/CRAN.package.sbert (may not be active yet) |
| Author: |
Mohammed Saqr 
[aut, cph],
Sonsoles López-Pernas
[aut, cre,
cph] |
| Maintainer: |
Sonsoles López-Pernas <sonsoles.lopez at uef.fi> |
| BugReports: |
https://github.com/sonsoleslp/sbert/issues |
| License: |
MIT + file LICENSE |
| Copyright: |
see file COPYRIGHTS |
| URL: |
https://sonsoles.me/sbert/, https://github.com/sonsoleslp/sbert |
| NeedsCompilation: |
no |
| SystemRequirements: |
ONNX Runtime (>= 1.25.1) |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
sbert results |
Documentation:
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