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HERBERT-P-30
ICB-UMA/HERBERT-P-30
HERBERT-P-30 is a feature extraction model from ICB-UMA. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
HERBERT-P is a contrastive-learning-based bi-encoder for medical entity normalization in Spanish, leveraging synonym and parent relationships from UMLS to enhance candidate retrieval for entity linking in clinical texts.
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jun 20, 2025
- Updated
- Jun 20, 2025
- SHA
- ac392779
Base models
Model card
From the Hugging Face model README
HERBERT: Leveraging UMLS Hierarchical Knowledge to Enhance Clinical Entity Normalization in Spanish
HERBERT-P is a contrastive-learning-based bi-encoder for medical entity normalization in Spanish, leveraging synonym and parent relationships from UMLS to enhance candidate retrieval for entity linking in clinical texts.
Key features:
- Base model: PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
- Trained with 30 positive pairs per anchor (synonyms + parents)
- Task: Normalization of disease, procedure, and symptom mentions to SNOMED-CT/UMLS codes.
- Domain: Spanish biomedical/clinical texts.
- Corpora: DisTEMIST, MedProcNER, SympTEMIST.
Benchmark Results
| Corpus | Top-1 | Top-5 | Top-25 | Top-200 |
|---|---|---|---|---|
| DisTEMIST | 0.588 | 0.723 | 0.803 | 0.867 |
| SympTEMIST | 0.635 | 0.784 | 0.882 | 0.946 |
| MedProcNER | 0.651 | 0.765 | 0.838 | 0.892 |
- Task
- Feature Extraction
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- es
- Created
- Jun 20, 2025
- Updated
- Jun 20, 2025