✅ Nearly triples its base on expert queries: R@10 0.487 vs 0.171 for stock BAAI/bge-m3 (+186%), on 4,998 held-out expert keyword queries against all 277,068 papers.
✅ Largest gains where retrieval is hardest: title-only documents +262% R@10, pre-1970 papers +307%.
✅ Trained on expert-written queries: NSR indexers hand-write a structured keyword abstract for every paper — no click logs, no synthetic questions.
✅ Drop-in: standard sentence-transformers bi-encoder, 1024-d cosine vectors, TEI-servable, MIT-licensed.

ncbi/MedCPT learned biomedical search from PubMed click logs. Nuclear physics has something rarer: for ~200,000 papers, an NSR indexer wrote a canonical description of what was measured and deduced. This model learned retrieval from 39,568 of those expert query→paper pairs, plus EXFOR experiment→paper links, in 33 minutes on one GPU.

First stage of a two-stage stack — pair it with the NSR Reranker for best results.

Details

Property	nsr-encoder
Type	Dense bi-encoder (single vector)
Total parameters	~568M
Backbone	BAAI/bge-m3
Output	1024-d normalized vector
Similarity	Cosine
Sequence length	128 query / 256 passage
Training signal	39.6k expert-written query→paper pairs (NSR keyword abstracts, EXFOR entries)
Built for	Search (query→document) over nuclear-physics literature
Expert-keyword R@10	0.487
License	MIT

Performance

Expert keyword queries (KW, n = 4,998) — an NSR indexer's structured keyword abstract as the query, the paper it describes as the gold. Every arm is scored on the same held-out queries, retrieved against the same 277,068 documents, with the same metric code — on a benchmark (NSR Eval) frozen before any training and split by paper.

Rank	Arm	R@1	R@10	nDCG@10
—	RRF(FTS + this model) (the production arm)	0.344	0.542	0.437
1	nsr-encoder (ours)	0.252	0.487	0.363
2	RRF(FTS + stock bge-m3)	0.212	0.282	0.244
3	Postgres FTS	0.161	0.165	0.163
4	BAAI/bge-m3 (stock base)	0.080	0.171	0.121

Where the gain lands — R@10 by segment, stock base vs this model:

Segment	n	stock bge-m3	nsr-encoder	Δ
title-only documents	4,141	0.127	0.458	+262%
has-abstract documents	857	0.383	0.631	+65%
pre-1970	602	0.076	0.311	+307%
1970–1999	2,686	0.152	0.507	+233%
2000+	1,710	0.232	0.519	+123%
journal articles	4,250	0.179	0.495	+176%
other reference types	748	0.120	0.447	+271%

Pre-registered gate — ≥ stock base on R@10 and nDCG@10 in every segment, ≥ 10% relative on blended KW — passed in all 8 segments.

EXFOR queries (EX, n = 4,997) — an experiment's title and reaction codes as the query. Near-saturated for every dense arm; this model still leads: R@1 0.911 vs 0.871 for stock bge-m3, R@10 0.966 vs 0.958.

Training


Objective	in-batch contrastive, dense only (`--unified_finetuning False`)
Trainer	FlagEmbedding `finetune.embedder.encoder_only.m3`
Pairs	39,568 — all EXFOR links + keyword abstracts stratified across era / richness / reference-type cells
Negatives / group	7 hard negatives mined from the base model's own embedding space + a lexical arm · group size 8
Batch / epochs / lr / temperature	16 / 1 / 1e-5 / 0.02
Precision	bf16
Hardware / wall-clock	1× NVIDIA L40S (`g6e.xlarge`) · 33.5 min

Split by paper; every benchmark paper is excluded as a query source and positive.

How to run

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("NYSgpt/nsr-encoder")

query = model.encode(["92Zr(n,γ) cross section, stellar nucleosynthesis"], normalize_embeddings=True)
docs = model.encode(
    ["Neutron capture cross sections of 92Zr and their astrophysical implications ..."],
    normalize_embeddings=True,
)
print(query @ docs.T)

Serves cleanly on Hugging Face TEI: --model-id NYSgpt/nsr-encoder.

For best quality, add the second stage: re-score this model's top-50 with the NSR Reranker.

📬 Contact

Questions, results, or a use case to share? Open a discussion in the Community tab.

Citation

bibtex

@misc{nsrencoder2026,
  title  = {NSR Encoder: dense retrieval over the Nuclear Science References corpus},
  author = {NYSgpt},
  year   = {2026},
  url    = {https://huggingface.co/NYSgpt/nsr-encoder}
}