lt-nobris-en

A sentence-transformer model fine-tuned for entity resolution in research security screening. Given two entity names, the model produces embeddings whose cosine similarity indicates whether they refer to the same organization.

Quickstart

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("nobris/lt-nobris-en")
emb1 = model.encode("Harbin Institute of Technology")
emb2 = model.encode("HIT")
similarity = util.cos_sim(emb1, emb2)  # ~0.85

Intended Use

This model is designed for matching entity names against restricted party lists in the context of research security and export control compliance. Primary use cases include:

• Screening research proposal affiliations against the US Consolidated Screening List (CSL), Section 1260H, Section 1286, and BIOSECURE Act entities
• Matching organization name variants across languages (English, Chinese, Russian)
• Resolving acronyms, aliases, subsidiaries, and transliterations to canonical entity names
• Matching institutional website domains (e.g., "hit.edu.cn") to organization names

Out-of-Scope Use

• Not a compliance decision system. This model produces similarity scores, not legal determinations. All matches should be reviewed by qualified compliance personnel.
• Not designed for individual/person name matching. The model is trained on organizational entity names.
• Not a general-purpose semantic similarity model. Performance on tasks outside entity resolution (e.g., sentence similarity, paraphrase detection) is not validated.

Model Details

Performance

Validation Set Metrics

Evaluated on a held-out validation set of 259,052 entity pairs (96,168 positive, 162,884 negative):

Average Precision (AP): 0.877 | Best Accuracy Threshold: 0.581 | Best F1 Threshold: 0.541

Acronym Discrimination

Evaluated on a 22,146-pair acronym-focused subset (acronym in at least one side):

Training Progression

Training Data

The model was fine-tuned on 689,049 training pairs from 12 curated data sources covering research security screening scenarios. All positive pairs represent confirmed same-entity matches; all negative pairs represent confirmed different entities.

Data Sources

Label Distribution

• Positive (same entity): 308,573 pairs (45%)
• Negative (different entity): 380,476 pairs (55%)

Languages Covered

The training data includes entity names in English, Simplified Chinese (zh-CN), Russian (Cyrillic), and 20+ additional languages from international sanctions lists (EU covers all official EU languages).

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nobris/lt-nobris-en")

# Encode entity name pairs
pairs = [
    ("Harbin Institute of Technology", "HIT"),              # Same entity
    ("Harbin Institute of Technology", "hit.edu.cn"),        # Domain match
    ("Harbin Institute of Technology", "哈尔滨工业大学"),       # Chinese name
    ("Harbin Institute of Technology", "Harbin Medical University"),  # Different
    ("CASC", "CASIC"),                                       # Confusable acronyms
]

for a, b in pairs:
    emb_a = model.encode(a)
    emb_b = model.encode(b)
    sim = model.similarity([emb_a], [emb_b])[0][0].item()
    print(f"{sim:.3f}  {a}  <->  {b}")

Recommended Thresholds

Bias, Risks, and Limitations

Known Limitations

• Acronym recall at high thresholds is limited. Acronym-to-name pairs (e.g., "CASC" ↔ "China Aerospace Science and Technology Corporation") often score 0.5-0.7 rather than 0.8+. Use threshold 0.5-0.6 for acronym-heavy screening.
• Domain matching is a new capability. The model can associate "hit.edu.cn" with "Harbin Institute of Technology", but coverage is limited to the ~109K organizations in ROR that have website links.
• Person names are excluded from training. The model is not suitable for individual name matching.
• Temporal drift. Sanctions lists and entity relationships change over time. The model reflects training data as of March 2026.

Bias Considerations

• The training data is heavily weighted toward Chinese and Russian entities due to the focus on US export control and sanctions screening. Performance on entities from other regions (e.g., Middle East, Africa) may be lower.
• The model inherits any biases present in the underlying sanctions lists and entity databases.
• False positives on legitimate Chinese academic institutions are a known risk. The model should not be used as the sole basis for restricting research collaborations.

Ethical Considerations

This model is intended to assist compliance professionals in screening research proposals against restricted party lists. It is not a decision-making system. All flagged matches should be reviewed by qualified personnel who can consider context, intent, and applicable regulations.

Research security screening affects international academic collaboration. Overly aggressive screening can harm legitimate scientific exchange. Users should calibrate thresholds to minimize both missed matches (compliance risk) and false positives (academic freedom risk).

Training Procedure

Hyperparameters

Framework Versions

• Python: 3.14
• Sentence Transformers: 5.3.0
• Transformers: 5.3.0
• PyTorch: 2.12.0+cu128

Licensing and Attribution

Model License

This model is released under the MIT License.

Base Model

Fine-tuned from dell-research-harvard/lt-wikidata-comp-en (LinkTransformer), itself fine-tuned from sentence-transformers/multi-qa-mpnet-base-dot-v1 (Apache 2.0).

Training Data Licenses

Important: The OpenSanctions training data is licensed CC BY-NC 4.0. If you intend to use this model commercially, you should either (a) obtain a commercial license from OpenSanctions, or (b) retrain without the OpenSanctions data.

Citation

This Model

@misc{nobris2026ltnobris,
  title={lt-nobris-en: Entity Resolution for Research Security Screening},
  author={Nobris},
  year={2026},
  url={https://huggingface.co/nobris/lt-nobris-en}
}

LinkTransformer (Base Model)

@misc{arora2023linktransformer,
  title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
  author={Abhishek Arora and Melissa Dell},
  year={2023},
  eprint={2309.00789},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

Sentence-Transformers

@inproceedings{reimers-2019-sentence-bert,
  title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
  author={Reimers, Nils and Gurevych, Iryna},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
  month={11},
  year={2019},
  publisher={Association for Computational Linguistics},
  url={https://arxiv.org/abs/1908.10084}
}

MultipleNegativesRankingLoss

@misc{oord2019representation,
  title={Representation Learning with Contrastive Predictive Coding},
  author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
  year={2019},
  eprint={1807.03748},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Model Card Authors

Nobris Research Security Team

Contact

For questions about this model, contact: [info@nobris.dev]