NSR
Collection
Retrieval over nuclear-physics literature — 277,068 Nuclear Science References: NSR Encoder, NSR Reranker, and NSR Eval, the frozen benchmark. • 3 items • Updated
- ✅ 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-transformersbi-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
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
@misc{nsrencoder2026,
title = {NSR Encoder: dense retrieval over the Nuclear Science References corpus},
author = {NYSgpt},
year = {2026},
url = {https://huggingface.co/NYSgpt/nsr-encoder}
}
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Model tree for NYSgpt/nsr-encoder
Base model
BAAI/bge-m3