Add model card

README.md

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+---
+license: apache-2.0
+language:
+- es
+base_model:
+- PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
+tags:
+- medical
+- spanish
+- bi-encoder
+- entity-linking
+- sapbert
+- umls
+- snomed-ct
+---
+
+# **MedProcNER-bi-encoder**
+
+## Model Description
+
+MedProcNER-bi-encoder is a domain-specific bi-encoder model for medical entity linking in Spanish, trained using synonym pairs from the MedProcNER corpus and SNOMED-CT (Fully Specified Name and preferred synonyms). The training data was curated from the gold standard corpus and enriched with knowledge-based synonyms to enhance entity normalization tasks.
+
+## 💡 Intended Use
+- **Domain**: Spanish Clinical NLP
+- **Tasks**: Entity linking of MedProcNER mentions to SNOMED-CT concepts
+- **Evaluated On**: MedProcNER (Gold Standard, Unseen Mentions, Unseen Codes)
+- **Users**: Researchers and developers focusing on specialized medical NEL
+
+### 💬 Definitions
+- **Gold Standard**: Mentions present in the training set (seen mentions and codes).
+- **Unseen Mentions**: Mentions that do not appear in training but reference known codes.
+- **Unseen Codes**: Mentions associated with SNOMED-CT codes never seen during training.
+
+## 📈 Performance Summary (Top-25 Accuracy)
+
+| Evaluation Split   | Top-25 Accuracy |
+|--------------------|-----------------|
+| Gold Standard      | 0.917 |
+| Unseen Mentions    | 0.831 |
+| Unseen Codes       | 0.808 |
+
+## 🧪 Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+model = AutoModel.from_pretrained("ICB-UMA/MedProcNER-bi-encoder")
+tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/MedProcNER-bi-encoder")
+
+mention = "insuficiencia renal aguda"
+inputs = tokenizer(mention, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+embedding = outputs.last_hidden_state[:, 0, :]
+print(embedding.shape)
+```
+
+Use with [Faiss](https://github.com/facebookresearch/faiss) or [`FaissEncoder`](https://github.com/ICB-UMA/KnowledgeGraph) for efficient retrieval.
+
+## ⚠️ Limitations
+
+- The model is specialized for MedProcNER mentions and may underperform in other domains or corpora.
+- Expert supervision is advised for clinical deployment.
+
+## 📚 Citation
+
+> Gallego, Fernando and López-García, Guillermo and Gasco, Luis and Krallinger, Martin and Veredas, Francisco J., Clinlinker-Kb: Clinical Entity Linking in Spanish with Knowledge-Graph Enhanced Biencoders. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4939986
+
+## Authors
+
+Fernando Gallego, Guillermo López-García, Luis Gasco-Sánchez, Martin Krallinger, Francisco J Veredas