Update README.md

# 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 [language] 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**:

- **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")

README.md

@@ -1,3 +1,18 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- sl
+metrics:
+- f1
+- precision
+- recall
+- confusion_matrix
+base_model:
+- google-bert/bert-base-cased
+pipeline_tag: token-classification
+tags:
+- NER
+- medical
+- symptom
+- extraction
+---