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.

See also

Useful links:

• https://sonsoles.me/sbert/

• https://github.com/sonsoleslp/sbert

• Report bugs at https://github.com/sonsoleslp/sbert/issues

Author

Maintainer: Sonsoles López-Pernas sonsoles.lopez@uef.fi (ORCID) [copyright holder]

Authors:

• Sonsoles López-Pernas sonsoles.lopez@uef.fi (ORCID) [copyright holder]

• Mohammed Saqr mohammed.saqr@uef.fi (ORCID) [copyright holder]