feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Pathology-Multi-209M

README.md

@@ -95,7 +95,7 @@ The NCBI Disease corpus is a gold-standard dataset containing 793 PubMed abstrac
 ### Installation
 
 ```bash
-pip install gliner==0.2.21
+pip install -q "gliner[tokenizers]"
 ```
 
 ### Usage
@@ -108,7 +108,7 @@ from transformers import pipeline
 model_name = "OpenMed/OpenMed-ZeroShot-NER-Pathology-Multi-209M"
 
 from gliner import GLiNER
-model = GLiNER.from_pretrained("OpenMed-ZeroShot-NER-Pathology-Multi-209M")
+model = GLiNER.from_pretrained(model_name)
 
 # Example usage with default entity types
 text = "Early detection of breast cancer improves survival rates."
@@ -161,8 +161,6 @@ This model is particularly useful for:
 - **Input**: Biomedical text
 - **Output**: Named entity predictions
 
-For more information about GLiNER, visit the [GLiNER repository](https://github.com/urchade/gliner).
-
 ## 📜 License
 
 Licensed under the Apache License 2.0. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.