Create README.md

README.md

@@ -0,0 +1,83 @@
+# Text-to-Text Transfer Transformer (T5) Quantized Model for Text Translation
+
+This repository hosts a quantized version of the T5 model, fine-tuned for text translation 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:** Text Translation  
+- **Dataset:** Hugging Face's `opus100`  
+- **Quantization:** Float16
+- **Supporting Languages:** English to French
+- **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-text-translator"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
+
+def translate_text(model, text, src_lang, tgt_lang):
+    input_text = f"translate {src_lang} to {tgt_lang}: {text}"
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+
+    # Generate translation
+    output_ids = model.generate(input_ids, max_length=50)
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Test Example
+test_sentences = {"en-fr": "Hello, what is your approach?"}
+
+for lang_pair, sentence in test_sentences.items():
+    src, tgt = lang_pair.split("-")
+    print(f"{src} → {tgt}: {translate_text(model, sentence, src, tgt)}")
+```
+
+## 📊 ROUGE Evaluation Results
+After fine-tuning the T5-Small model for text translation, we obtained the following ROUGE scores:
+
+| **Metric**  | **Score** | **Meaning**  |
+|------------|---------|--------------------------------------------------------------|
+| **ROUGE-1**  | 0.4673 (~46%) | Measures overlap of unigrams (single words) between the reference and generated text. |
+| **ROUGE-2**  | 0.2486 (~24%) | Measures overlap of bigrams (two-word phrases), indicating coherence and fluency. |
+| **ROUGE-L**  | 0.4595 (~45%) | Measures longest matching word sequences, testing sentence structure preservation. |
+| **ROUGE-Lsum**  | 0.4620 (~46%) | Similar to ROUGE-L but optimized for summarization tasks. |
+
+## Fine-Tuning Details
+### Dataset
+The Hugging Face's `opus100` dataset was used, containing different types of translations of languages.
+
+### 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.