Update README.md

README.md

@@ -20,10 +20,10 @@ tags:
 - english
 ---
 
-# Slovenian Medical NER
+# English Medical NER
 
 ## Use
-- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Slovenian language.
+- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the English language.
 - **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
 - **Supported Entity Types**:
   - `PROBLEM`: Diseases, symptoms, and medical conditions.
@@ -31,7 +31,7 @@ tags:
   - `TREATMENT`: Medications, therapies, and other medical interventions.
 
 ## Training Data
-- **Data Sources**: Annotated datasets, including clinical data and translations of English medical text into Slovenian.
+- **Data Sources**: Annotated datasets, including clinical data in English.
 - **Data Augmentation**: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures.
 - **Dataset Split**:
   - **Training Set**: 80%
@@ -45,7 +45,8 @@ tags:
   - **Batch Size**: 64
   - **Epochs**: 200
   - **Loss Function**: Focal Loss to handle class imbalance
-- **Frameworks**: PyTorch, Hugging Face Transformers, SimpleTransformers
+- **Frameworks
+**: PyTorch, Hugging Face Transformers, SimpleTransformers
 
 ## How to Use
 You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference: