Update README.md

README.md

@@ -6,4 +6,45 @@ base_model:
 - PlanTL-GOB-ES/roberta-base-biomedical-clinical-es
 tags:
 - medical
----
+---
+
+
+# **ClinLinker-KB-GP**
+
+## Model Description
+ClinLinker-KB-GP is a state-of-the-art model designed for medical entity linking (MEL) in Spanish, specifically optimized for tasks in the clinical domain. It is based on bi-encoder models enriched with knowledge from medical knowledge graphs like UMLS and SNOMED-CT. This model leverages contrastive learning techniques to enhance the quality of embedding spaces and improve the retrieval of relevant concepts for medical entities mentioned in clinical text.
+
+The "GP" in ClinLinker-KB-GP stands for **Grand Parents**. In this model, hierarchical relationships were used, including **parent** and **grandparent** terms as positive candidates. This strategy improves the embedding quality by incorporating terms that are conceptually close at different levels in the knowledge graph, enhancing the linking process.
+
+## Intended Use
+- **Domain:** Clinical Natural Language Processing (NLP) for medical entity linking in Spanish.
+- **Primary Tasks:** Recognizing and normalizing medical entities such as diseases, symptoms, and procedures from clinical texts and linking them to their corresponding standardized terminologies in SNOMED-CT.
+- **Corpora Evaluated:** ClinLinker-KB-GP was tested on several Spanish medical corpora including DisTEMIST (for diseases), MedProcNER (for procedures), and SympTEMIST (for symptoms). It achieved top-tier performance, with top-200 accuracy values of 0.969 in SympTEMIST, 0.943 in MedProcNER, and 0.912 in DisTEMIST.
+- **Target Users:** Researchers, healthcare practitioners, and developers working with Spanish medical data for entity recognition and normalization tasks.
+
+## Performance
+ClinLinker-KB-GP achieved the following key results:
+- **Top-200 Accuracy:**
+  - DisTEMIST: 91.2%
+  - MedProcNER: 94.3%
+  - SympTEMIST: 96.9%
+- **Top-25 Accuracy:** 
+  - The model achieves up to 86.4% accuracy in retrieving the correct concept in the top-25 candidates for disease and procedure normalization tasks.
+- **Cross-Encoder Integration:** ClinLinker-KB-GP is particularly effective when used with a cross-encoder for reranking candidate concepts, leading to improved accuracy in zero-shot and few-shot learning scenarios.
+
+## Technical Details
+- **Architecture:** The model is a bi-encoder with contrastive learning, designed to generate embeddings for clinical terms, using the relational structure of medical concepts extracted from the UMLS and SNOMED-CT knowledge bases.
+- **Training Strategy:** ClinLinker-KB-GP was trained with a hierarchical relationship structure, incorporating "parent" and "grandparent" nodes from medical knowledge graphs to enhance the embeddings’ quality. The training process also utilizes hard negative mining techniques to optimize candidate retrieval.
+
+## Usage
+Users can utilize our pre-trained model in several ways:
+- By using the provided **FaissEncoder** class to perform efficient entity linking with FAISS-based search.
+- By training their own Bi-encoder model for medical entity linking using our framework available on GitHub:  
+  [https://github.com/ICB-UMA/ClinLinker-KB](https://github.com/ICB-UMA/ClinLinker-KB)
+- Alternatively, users can load the model directly with Hugging Face’s `AutoModel` and `AutoTokenizer` for flexible integration in custom pipelines:
+
+  ```python
+  from transformers import AutoModel, AutoTokenizer
+
+  model = AutoModel.from_pretrained("ICB-UMA/ClinLinker-KB-GP")
+  tokenizer = AutoTokenizer.from_pretrained("ICB-UMA/ClinLinker-KB-GP")