This model is a fine-tuned version of distilbert-base-uncased trained to classify medical and health-related questions into 20 distinct medical specialties (e.g., Cardiology, Pediatrics, Psychiatry, Surgery).

Intended Use

Primary Use Case: Triage systems and automatic tag generation for medical Q&A platforms.
Safety Notice: This model is designed strictly for categorization and routing purposes. It does not provide medical diagnosis or treatment advice.

Dataset Information

Dataset: openlifescienceai/medmcqa
License: Apache 2.0
Classes (20): Anaesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine, Gynaecology & Obstetrics, Medicine, Microbiology, Ophthalmology, Orthopaedics, Pathology, Pediatrics, Pharmacology, Physiology, Psychiatry, Radiology, Skin, Social & Preventive Medicine, Surgery.

Training Hyperparameters

Base Model: distilbert-base-uncased
Epochs: 3
Batch Size: 16
Learning Rate: 2e-5
Optimizer: AdamW

Evaluation Metrics

Accuracy: ~82.4%
Weighted F1-Score: ~0.821

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "mahmoodulhassan23/medical-question-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

question = "Child presenting with high grade fever, rash, and cough."
inputs = tokenizer(question, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    pred_class = torch.argmax(outputs.logits, dim=-1).item()

print(f"Predicted Class ID: {pred_class}")