Feature Extraction
Transformers
Safetensors
Spanish
roberta
contrastive-learning
Spanish-UMLS
Hierarchical-enrichment
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/HERBERT-GP-30")
model = AutoModel.from_pretrained("ICB-UMA/HERBERT-GP-30", device_map="auto")Quick Links
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 |
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Model tree for ICB-UMA/HERBERT-GP-30
Base model
PlanTL-GOB-ES/roberta-base-biomedical-es
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ICB-UMA/HERBERT-GP-30")