---
library_name: transformers
tags:
- contrastive-learning
- Spanish-UMLS
- Hierarchical-enrichment
license: mit
language:
- es
base_model:
- PlanTL-GOB-ES/roberta-base-biomedical-es
---

# HERBERT: Leveraging UMLS Hierarchical Knowledge to Enhance Clinical Entity Normalization in Spanish

**HERBERT-GP** is a contrastive-learning-based bi-encoder for medical entity normalization in Spanish.  
It leverages hierarchical relationships from UMLS (parents and grandparents) to enhance the candidate retrieval step for entity linking in Spanish clinical texts.

**Key features:**
- Base model: [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es)
- Trained with 15 positive pairs per anchor using synonyms, parents, and grandparents from UMLS/SNOMED-CT.
- Task: Normalization of disease, procedure, and symptom mentions to SNOMED-CT/UMLS codes.
- Domain: Spanish biomedical/clinical texts.
- Corpora: DisTEMIST, MedProcNER, SympTEMIST.

---
## Evaluation (top-k accuracy):

| Corpus      | Top-1  | Top-5  | Top-25 | Top-200 |
|-------------|--------|--------|--------|---------|
| DisTEMIST   | 0.574  | 0.720  | 0.803  | 0.871   |
| SympTEMIST  | 0.630  | 0.779  | 0.886  | 0.949   |
| MedProcNER  | 0.655  | 0.767  | 0.840  | 0.894   |