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# Text-to-Text Transfer Transformer Quantized Model for Drug Reports Summarization
This repository hosts a quantized version of the T5 model, fine-tuned for text summarization 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:** Drug Report Summarization
- **Dataset:** Hugging Face's `cnn_dailymail'
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/t5-summarization-for-drug-reports"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
def test_summarization(model, tokenizer):
user_text = input("\nEnter your text for summarization:\n")
input_text = "summarize: " + user_text
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)
output = model.generate(
**inputs,
max_new_tokens=100,
num_beams=5,
length_penalty=0.8,
early_stopping=True
)
summary = tokenizer.decode(output[0], skip_special_tokens=True)
return summary
print("\n📝 **Model Summary:**")
print(test_summarization(model, tokenizer))
```
# 📊 ROUGE Evaluation Results
After fine-tuning the **T5-Small** model for text summarization, we obtained the following **ROUGE** scores:
| **Metric** | **Score** | **Meaning** |
|-------------|-----------|-------------|
| **ROUGE-1** | **0.3061** (~30%) | Measures overlap of **unigrams (single words)** between the reference and generated summary. |
| **ROUGE-2** | **0.1241** (~12%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
| **ROUGE-L** | **0.2233** (~22%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
| **ROUGE-Lsum** | **0.2620** (~26%) | Similar to ROUGE-L but optimized for summarization tasks. |
## Fine-Tuning Details
### Dataset
The Hugging Face's `cnn_dailymail` dataset was used, containing the text and their summarization examples.
### Training
- Number of epochs: 3
- Batch size: 4
- Evaluation strategy: epoch
- Learning rate: 3e-5
### 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.
- 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.
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