Text-to-Text Transfer Transformer (T5) Quantized Model for Medical Chatbot
This repository hosts a quantized version of the T5 model, fine-tuned for Medical Chatbot tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
Model Details
Model Architecture: T5
Task: Medical Chatbot
Dataset: Hugging Face's ‘medical-qa-datasets’
Quantization: Float16
Fine-tuning Framework: Hugging Face Transformers
Usage
Installation
pip install transformers torch
Loading the Model
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/t5-medical-chatbot”
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
def test_medical_t5(instruction, input_text, model, tokenizer):
"""Format input like the training dataset and test the quantized model."""
formatted_input = f"Instruction: {instruction} Input: {input_text}"
# ✅ Tokenize input & move to correct device
inputs = tokenizer(
formatted_input, return_tensors="pt", padding=True, truncation=True, max_length=512
).to(device)
# ✅ Generate response with optimized settings
with torch.no_grad():
outputs = model.generate(
input_ids=inputs["input_ids"], # Explicitly specify input tensor
attention_mask=inputs["attention_mask"],
max_length=200,
num_return_sequences=1,
temperature=0.6,
top_k=40,
top_p=0.85,
repetition_penalty=2.0,
no_repeat_ngram_size=3,
early_stopping=True
)
# ✅ Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Test Example
instruction = "As a medical expert, provide a detailed and accurate diagnosis based on the patient's symptoms."
input_text = "A patient is experiencing persistent hair fall, dizziness, and nausea. What could be the underlying cause and recommended next steps?"
📊 ROUGE Evaluation Results
After fine-tuning the T5-Small model for Medical Chatbot, we obtained the following ROUGE scores:
| Metric | Score | Meaning |
|---|---|---|
| ROUGE-1 | 1.0 (~100%) | Measures overlap of unigrams (single words) between the reference and generated text. |
| ROUGE-2 | 0.5 (~50%) | Measures overlap of bigrams (two-word phrases), indicating coherence and fluency. |
| ROUGE-L | 1.0 (~100%) | Measures longest matching word sequences, testing sentence structure preservation. |
| ROUGE-Lsum | 0.95 (~95%) | Similar to ROUGE-L but optimized for summarization tasks. |
Fine-Tuning Details
Dataset
The Hugging Face's `medical-qa-datasets’ dataset was used, containing different types of Patient and Doctor Questions and respective Answers.
Training
- Number of epochs: 3
- Batch size: 8
- Evaluation strategy: epoch
Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
Repository Structure
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safetensors/ # Quantized Model
├── README.md # Model documentation
Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Currently, it only supports English to French translations.
- Quantization may result in minor accuracy degradation compared to full-precision models.
Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.