MedNER-TR

Turkish Medical Named Entity Recognition System

Performance

• F1 Score: 99.49%
• Precision: 99.49%
• Recall: 99.49%
• Accuracy: 99.76%

Links

• Demo: https://huggingface.co/spaces/tugrulkaya/medner-tr-demo
• Model: https://huggingface.co/tugrulkaya/medner-tr

Quick Start

from transformers import pipeline

ner = pipeline("token-classification", 
               model="tugrulkaya/medner-tr",
               aggregation_strategy="simple")

text = "Hastaya Parol 500mg baslandi."
results = ner(text)

Supported Entities

• ILAC (Medications)
• HASTALIK (Diseases)
• SEMPTOM (Symptoms)
• ORGAN (Organs)
• TEST (Medical Tests)

Installation

pip install transformers torch

Examples

Example 1

text = "Hastaya Parol 500mg baslandi."
results = ner(text)
# Output: ILAC: Parol

Example 2

text = "Hasta ates, oksuruk ile basvurdu."
results = ner(text)
# Output: SEMPTOM: ates, SEMPTOM: oksuruk

License

MIT License - Free for commercial and research use

Contact

• GitHub: https://github.com/tugrulkaya/medner-tr
• Hugging Face: https://huggingface.co/tugrulkaya/medner-tr