PatriSBERT-STS
A Sentence-BERT model fine-tuned for semantic textual similarity on patristic and biblical Latin texts. It is designed to detect and measure text reuse between early Christian writings and the Vulgate Bible.
/!\ Work in progress: this is a draft version of PatriSBERT previously released for experiments. Its current performances are provisional.
Usage
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("tdelaselle/PatriSBERT-STS")
sentences = [
"In principio erat Verbum",
"Et Verbum caro factum est",
]
embeddings = model.encode(sentences)
similarity = util.cos_sim(embeddings[0], embeddings[1])
print(f"Cosine similarity: {similarity.item():.4f}")
Training
- Base model: PatriSBERT-NLI (SBERT model trained on NLI-type latin biblical reuses dataset) from the PatriBERT model (BERT pre-trained on latin patristic texts)
- Task: Semantic textual similarity (STS) via triplet fine-tuning
- Dataset: Latin biblical reuse triplets
Evaluation
See the eval/ folder for evaluation metrics on the held-out test set.
Citation
If you use this model, please cite:
@misc{patriSBERT2026,
author = {TdelaSelle},
title = {PatriSBERT-STS},
year = {2026},
url = {https://huggingface.co/TdelaSelle/PatriSBERT-STS}
}