metadata
language: en
pipeline_tag: text-classification
library_name: transformers
tags:
- medical
- classification
- healthcare
- clinicalbert
- symptom-checker
- text-classification
license: apache-2.0
datasets:
- qilex/Symptom2Disease
- niyarrbarman/symptom-disease-dataset
metrics:
- accuracy
model-index:
- name: SymbiPredict-ClinicalBERT
results:
- task:
type: text-classification
name: Disease Prediction
metrics:
- type: loss
value: 0.2577
base_model:
- emilyalsentzer/Bio_ClinicalBERT
🏥 SymbiPredict: ClinicalBERT Symptom-to-Disease Classifier
This model is a fine-tuned version of Bio_ClinicalBERT, optimized to predict diseases based on natural language descriptions of symptoms.
It has been trained on a massive merged dataset of over 96,000 patient cases covering 115+ unique medical conditions.
📊 Model Performance
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.4108 | 0.3452 |
| 2 | 0.3092 | 0.2852 |
| 3 | 0.2526 | 0.2577 |
The model achieves a final validation loss of 0.2577, demonstrating high confidence and generalization capabilities across 115 disease classes.
🚀 How to Use (Python)
You can use this model directly with the Hugging Face pipeline.
from transformers import pipeline
# Load the pipeline
classifier = pipeline("text-classification", model="Iloriayomide/Symptom_Prediction", top_k=3)
# Test with symptoms
symptoms = "I have a severe headache, sensitivity to light, and I feel nauseous."
prediction = classifier(symptoms)
print(prediction)