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ICB-UMA
/
ClinLinker

PyTorch
Spanish
roberta
medical
spanish
bi-encoder
entity-linking
sapbert
umls
snomed-ct
Model card Files
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Update README.md

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README.md CHANGED
@@ -26,7 +26,7 @@ ClinLinker is a state-of-the-art bi-encoder model for medical entity linking (ME
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  - **Evaluated On**: DisTEMIST, MedProcNER, SympTEMIST
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  - **Users**: Researchers and practitioners working in clinical NLP
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- ## Performance Summary (Top-25 Accuracy)
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  | Model | DisTEMIST | MedProcNER | SympTEMIST |
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  |--------------------|-----------|------------|------------|
@@ -57,7 +57,7 @@ print(embedding.shape)
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  For scalable retrieval, use [Faiss](https://github.com/facebookresearch/faiss) or the [`FaissEncoder`](https://github.com/ICB-UMA/KnowledgeGraph) class.
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- ## Limitations
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  - The model is optimized for Spanish clinical data and may underperform outside this domain.
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  - Expert validation is advised in critical applications.
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  - **Evaluated On**: DisTEMIST, MedProcNER, SympTEMIST
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  - **Users**: Researchers and practitioners working in clinical NLP
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+ ## 📈 Performance Summary (Top-25 Accuracy)
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  | Model | DisTEMIST | MedProcNER | SympTEMIST |
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  |--------------------|-----------|------------|------------|
 
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  For scalable retrieval, use [Faiss](https://github.com/facebookresearch/faiss) or the [`FaissEncoder`](https://github.com/ICB-UMA/KnowledgeGraph) class.
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+ ## ⚠️ Limitations
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  - The model is optimized for Spanish clinical data and may underperform outside this domain.
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  - Expert validation is advised in critical applications.
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