---
base_model: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
pipeline_tag: sentence-similarity
---

# THIRAWAT-SapBERT

**THIRAWAT-SapBERT** is a fine-tuned ColBERTv1-style late-interaction reranker for drug terminology mapping to standardized OMOP Drug concepts, especially RxNorm and RxNorm Extension.

THIRAWAT stands for **Terminology Harmonization using Late-Interaction Reranker With Alignment-tuned Transformers**. This model is designed to rerank candidate drug concepts generated by a first-stage retriever, not to perform full terminology mapping by itself.

It is best used through **THIRAWAT Mapper**:

https://github.com/sidataplus/THIRAWAT-mapper


THIRAWAT-SapBERT was evaluated as part of the full **THIRAWAT Mapper** pipeline. In plain terms:

* **Hits@1** means the correct mapping was ranked **first**.
* **Hits@3** means the correct mapping appeared in the **top 3 suggestions**, which is useful for human review.

Across the evaluated datasets, the full pipeline ranked the correct drug concept first in **85.9%–94.2%** of cases, and placed it within the top 3 in **93.0%–96.4%** of cases.

| Dataset                    | Correct at #1 | Correct in top 3 |
| -------------------------- | ------------: | ---------------: |
| Branded Drugs              |         94.2% |            96.4% |
| Clinical Drugs             |         85.9% |            93.0% |
| Thai Medicines Terminology |         86.8% |            95.4% |

These are **full-pipeline results**, using SapBERT-XLMR retrieval, THIRAWAT-SapBERT reranking with BiMaxSim, and deterministic tie-breaking. The deterministic tie-breaker is implemented in THIRAWAT Mapper, not inside this Hugging Face model checkpoint.


This model is described in:

**Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering**
Healthcare Informatics Research, 2026;32(2):156–165
DOI: https://doi.org/10.4258/hir.2026.32.2.156

Please cite the paper if you use this model or THIRAWAT Mapper.


## Intended Use

Use this model for reranking candidate OMOP/RxNorm drug concepts in medication terminology mapping workflows.

Typical pipeline:

1. Retrieve candidate concepts with SapBERT-XLMR or another biomedical retriever.
2. Rerank candidates with THIRAWAT-SapBERT.
3. Score late-interaction matches with inference-time **BiMaxSim**.
4. Apply deterministic tie-breaking for near-ties using drug-specific cues such as strength, dosage form/route, release characteristics, and brand annotations.
5. Review the final mappings before production use.

This model is intended for terminology mapping and mapping assistance. It is not a prescribing tool, medication safety checker, or standalone clinical decision support model.

## Example: THIRAWAT Mapper CLI

```bash
pip install thirawat-mapper
```

Interactive query:

```bash
thirawat infer query \
  --db data/lancedb/db \
  --table concepts_drug \
  --device auto \
  --reranker-id sidataplus/THIRAWAT-SapBERT
```

Bulk mapping:

```bash
thirawat infer bulk \
  --db data/lancedb/db \
  --table concepts_drug \
  --input data/usagi.csv \
  --out runs/mapping \
  --candidate-topk 200 \
  --n-limit 20 \
  --device auto \
  --reranker-id sidataplus/THIRAWAT-SapBERT
```

## Model Details

* **Base model:** `cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR`
* **Architecture:** ColBERTv1-style late-interaction reranker
* **Training objective:** one-sided MaxSim
* **Recommended inference scoring:** BiMaxSim
* **Projection dimension:** 128
* **Max query length:** 96 tokens
* **Max candidate length:** 96 tokens
* **Primary domain:** OMOP Drug mapping to RxNorm / RxNorm Extension


## Limitations

* Evaluated primarily for drug terminology mapping.
* Not yet validated for other OMOP domains such as Condition, Procedure, Measurement, or Observation.
* Performance depends on the target vocabulary and ATHENA/RxNorm coverage.
* Local brands may be mapped to generic ingredient-strength-form concepts when no corresponding branded target exists.
* Remaining errors often involve clinically close candidates, such as strength, form, release, combination-product, or brand-coverage mismatches.
* Automated mappings should be reviewed before use in production OMOP ETL or research workflows.

## Citation

```bibtex
@article{adulyanukosol2026thirawat,
  title = {Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering},
  author = {Adulyanukosol, Natthawut and Chaisutyakorn, Krittaphas and Sombutjaroan, Saknarong and Kanjanapong, Suchanan and Suriyaphol, Prapat},
  journal = {Healthcare Informatics Research},
  year = {2026},
  volume = {32},
  number = {2},
  pages = {156--165},
  doi = {10.4258/hir.2026.32.2.156}
}
```
Please also cite the original SapBERT paper.

```bibtex
@inproceedings{liu2021learning,
	title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking},
	author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
	booktitle={Proceedings of ACL-IJCNLP 2021},
	month = aug,
	year={2021}
}
```