nsr-encoder

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NSR Encoder

Dense retrieval over nuclear-physics literature (277,068 Nuclear Science References).

NSR Collection | NSR Reranker

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• ✅ 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.

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Details

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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.

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

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.

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Training

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

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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.

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📬 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}
}