A sentence-transformer model fine-tuned for academic citation recommendation. Given a passage of academic writing, this model finds the most relevant papers to cite.

Model Details

Base model: microsoft/MiniLM-L6-v2 (via all-MiniLM-L6-v2)
Dimensions: 384 (Matryoshka: 128/256/384)
Training: CachedMultipleNegativesRankingLoss with hard negatives
Training data: 64K citation context → cited paper pairs from academic papers

Performance

Evaluated on a benchmark of 3,420 citation contexts across 100 source papers:

Method	MRR	R@1	R@10
MiniLM-FT v4 (neural)	0.400	27.5%	72.0%
MiniLM-FT v4 (hybrid)	0.428	30.2%	74.8%
Cloud model (hybrid)	0.550	38.5%	88.5%

For best results, use with inCite in hybrid mode (neural + BM25 fusion).

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("galenphall/minilm-citation-v4")

# Embed a query (your writing)
query_embedding = model.encode("The relationship between CO2 emissions and temperature...")

# Embed papers as: "title. authors. year. journal. abstract"
paper_embedding = model.encode("Global Warming Effects. Smith and Jones. 2023. Nature. We study...")

Or use with inCite directly:

bash

pip install incite
incite setup
incite recommend "your text here" -k 10

Citation

bibtex

@software{incite2025,
  author = {Hall, Galen},
  title = {inCite: Local-First Citation Recommendation},
  year = {2025},
  url = {https://github.com/galenphall/incite},
  license = {Apache-2.0}
}