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# Paraphrase Generation with Text-to-Text Transfer Transformer
## 📌 Overview
This repository hosts the quantized version of the T5 model fine-tuned for Paraphrase Generation. The model has been trained on the chatgpt-paraphrases dataset from Hugging Face to enhance grammatical accuracy in given text inputs. The model is quantized to Float16 (FP16) to optimize inference speed and efficiency while maintaining high performance.
## 🏗 Model Details
- **Model Architecture:** t5-small
- **Task:** Paraphrase Generation
- **Dataset:** Hugging Face's `chatgpt-paraphrases`
- **Quantization:** Float16 (FP16) for optimized inference
- **Fine-tuning Framework:** Hugging Face Transformers
## 🚀 Usage
### Installation
```bash
pip install transformers torch
```
### Loading the Model
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, pipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/t5-paraphrase-generation"
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)
```
### Grammar Correction Inference
```python
paraphrase_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
test_text = "The quick brown fox jumps over the lazy dog"
# Generate paraphrases
results = paraphrase_pipeline(
test_text,
max_length=256,
truncation=True,
num_return_sequences=5,
do_sample=True,
top_k=50,
temperature=0.7
)
print("Original Text:", test_text)
print("\nParaphrased Outputs:")
for i, output in enumerate(results):
generated_text = output["generated_text"] if isinstance(output, dict) else str(output)
print(f"{i+1}. {generated_text.strip()}")
```
# 📊 ROUGE Evaluation Results
After fine-tuning the **T5-Small** model for paraphrase generation, we obtained the following **ROUGE** scores:
| **Metric** | **Score** | **Meaning** |
|-------------|-----------|-------------|
| **ROUGE-1** | **0.7777** (~78%) | Measures overlap of **unigrams (single words)** between the reference and generated summary. |
| **ROUGE-2** | **0.5** (~50%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
| **ROUGE-L** | **0.7777** (~78%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
| **ROUGE-Lsum** | **0.7777** (~78%) | Similar to ROUGE-L but optimized for summarization tasks. |
## ⚡ Quantization Details
Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
## 📂 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 struggle with highly ambiguous sentences.
- Quantization may lead to slight degradation in accuracy compared to full-precision models.
- Performance may vary across different writing styles and sentence structures.
## 🤝 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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