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README.md

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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=quantized_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.