E5-large Latin Intertextuality Embedding Model
This model is a fine-tuned version of intfloat/multilingual-e5-large for generating embeddings of Latin texts to detect intertextual relationships.
The model is part of the Loci Similes benchmark setup (Schelb et al., 2026), evaluated on expert-verified Latin intertextual links. It is designed to work with the LociSimiles Python package API: https://julianschelb.github.io/locisimiles/api/.
Model Description
- Task: Sentence embedding for detecting intertextual links between classical Latin authors
- Model type: Sentence Transformer (Embedding Model)
- Base model: intfloat/multilingual-e5-large
- Negative sampling ratio: 1_pos_to_10_neg
- Fold uploaded: 0
- Language: Latin
- License: Apache 2.0
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("julian-schelb/multilingual-e5-large-emb-lat-intertext-v1")
query_embedding = model.encode("Query: arma virumque cano")
candidate_embedding = model.encode("Candidate: arma virumque cano troiae qui primus ab oris")
If prompts are configured in the model, prefer:
prompt_name="query"for query textsprompt_name="match"for candidate texts
Citation
@misc{schelb2026locisimilesbenchmarkextracting,
title={Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature},
author={Julian Schelb and Michael Wittweiler and Marie Revellio and Barbara Feichtinger and Andreas Spitz},
year={2026},
eprint={2601.07533},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2601.07533},
}