Update README.md

README.md

@@ -15,4 +15,49 @@ tags:
 - medical
 - symptom
 - extraction
----
+---
+# Slovenian 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.
+- **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
+- **Supported Entity Types**:
+  - `PROBLEM`: Diseases, symptoms, and medical conditions.
+  - `TEST`: Diagnostic procedures and laboratory tests.
+  - `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 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%
+  - **Validation Set**: 10%
+  - **Test Set**: 10%
+
+## Model Training
+- **Training Configuration**:
+  - **Optimizer**: AdamW
+  - **Learning Rate**: 3e-5
+  - **Batch Size**: 64
+  - **Epochs**: 200
+  - **Loss Function**: Focal Loss to handle class imbalance
+- **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:
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+model_name = "rigonsallauka/slovenian_medical_ner"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Sample text for inference
+text = "Pacient se je pritoževal zaradi hudih glavobolov in slabosti, ki sta trajala dva dni."
+
+# Tokenize the input text
+inputs = tokenizer(text, return_tensors="pt")