Branis333/symptom-gpt2-chatbot

🏥 Medical Symptom Chatbot - GPT2 Fine-tuned

A specialized GPT-2 model fine-tuned on medical Q&A data to assist with symptom analysis, disease information, and health-related questions.

🎯 Model Description

This model is based on GPT-2 and has been fine-tuned on a comprehensive medical dataset combining:

• Symptom-Disease mappings with descriptions and precautions
• MedQuAD dataset with expert medical Q&A pairs
• Custom medical knowledge base

⚠️ IMPORTANT DISCLAIMER: This model is for informational and educational purposes only. Always consult qualified healthcare professionals for medical advice, diagnosis, or treatment.

📊 Training Details

Dataset Statistics

• Total Training Samples: 8,437
• Validation Samples: 938
• Total Dataset Size: 9,375 medical Q&A pairs

Training Configuration

• Base Model: GPT-2 (124M parameters)
• Training Epochs: 10
• Batch Size: 4
• Learning Rate: 3e-5
• Optimizer: AdamW
• Max Sequence Length: 512 tokens
• Hardware: NVIDIA GPU (CUDA enabled)
• Training Time: ~3.5 hours

Performance Metrics

Epoch	Train Loss	Val Loss	Perplexity
1	0.5518	0.4664	1.59
2	0.4553	0.4366	1.55
3	0.4162	0.4196	1.52
4	0.3865	0.4088	1.51
5	0.3621	0.4015	1.49
6	0.3415	0.3975	1.49
7	0.3233	0.3988	1.49
8	0.3069	0.3984	1.49
9	0.2917	0.3977	1.49
10	0.2781	0.4024	1.50

Final Model Performance:

• ✅ Training Loss: 0.2781
• ✅ Validation Loss: 0.4024
• ✅ Validation Perplexity: 1.50

🚀 Usage

Basic Usage

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch

# Load model and tokenizer
model_name = "Branis333/symptom-gpt2-chatbot"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Generate response
question = "I have fever and cough. What could this be?"
prompt = f"User: {question} Bot:"

inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
answer = response.split("Bot:")[-1].strip()
print(answer)

💡 Example Queries

Symptom Analysis

User: I have fever and cough. What could this be?
Bot: You may be experiencing a respiratory infection...

Disease Information

User: What are the symptoms of diabetes?
Bot: Common symptoms include increased thirst, frequent urination...

📁 Dataset Sources

1. Kaggle Symptom-Disease Dataset - Disease descriptions, symptom mappings, precautions
2. MedQuAD - Expert-curated medical Q&A from multiple domains

⚠️ Limitations

1. Not a Medical Professional: Cannot replace professional medical advice
2. Training Data Bias: Limited to information in training data
3. Hallucination Risk: May generate plausible but incorrect information
4. Language: Primarily English medical texts

🔒 Ethical Considerations

• Informational Only: Should not be used for self-diagnosis
• Professional Consultation Required: Always seek medical professionals for health concerns
• Verification: Cross-check any medical information with reliable sources

📄 License

MIT License - Free to use with attribution

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Built with ❤️ using Hugging Face Transformers

Last Updated: October 2024