Downloads · 30 days
17
21% of all-time downloads
HERBERT-GP-30
ICB-UMA/HERBERT-GP-30
HERBERT-GP-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-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 f…
All-time downloads
80
Public
Parameters
126M
504 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors504 MB · 99%
At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jun 20, 2025
- Updated
- Jun 20, 2025
- SHA
- 324e5fff
Base models
Model card
From the Hugging Face model README
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
- Trained with 30 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 | 0.585 | 0.727 | 0.808 | 0.871 |
| SympTEMIST | 0.632 | 0.783 | 0.884 | 0.948 |
| MedProcNER | 0.655 | 0.770 | 0.840 | 0.891 |
- Task
- Feature Extraction
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- es
- Created
- Jun 20, 2025
- Updated
- Jun 20, 2025