Update README.md

README.md

@@ -1,3 +1,111 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+- es
+---
+# Neural Machine Translation with Attention 🚀
+
+A PyTorch implementation of a Sequence-to-Sequence model with Attention for English-Spanish translation.
+
+![Python](https://img.shields.io/badge/Python-3.8%2B-blue)
+![PyTorch](https://img.shields.io/badge/PyTorch-2.0%2B-orange)
+![License](https://img.shields.io/badge/license-MIT-green)
+
+## 🌟 Features
+
+- **Bidirectional GRU Encoder**: Captures context from both directions of the input sequence
+- **Attention Mechanism**: Helps the model focus on relevant parts of the input sequence
+- **Teacher Forcing**: Implements curriculum learning for better training stability
+- **Dynamic Batching**: Efficient training with variable sequence lengths
+- **Hugging Face Integration**: Uses MarianTokenizer for robust text processing
+
+## 🏗️ Architecture
+
+The model consists of three main components:
+
+1. **Encoder**: Bidirectional GRU network that processes input sequences
+2. **Attention**: Computes attention weights for each encoder state
+3. **Decoder**: GRU network that generates translations using attention context
+
+```plaintext
+Input → Encoder → Attention → Decoder → Translation
+      ↑          ↑          ↑
+      Embeddings Context    Attention Weights
+```
+
+## 🚀 Quick Start
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/nmt-attention.git
+cd nmt-attention
+```
+
+2. Install dependencies:
+```bash
+pip install torch transformers datasets
+```
+
+3. Train the model:
+```python
+python train.py
+```
+
+4. Translate text:
+```python
+from translate import translate
+text = "How are you?"
+translated = translate(model, text, tokenizer)
+print(translated)
+
+# Loading a saved model
+model = Seq2Seq(encoder, decoder, device)
+model.load_state_dict(torch.load('LSTM_text_generator.pth'))
+model.eval()
+```
+
+## 📊 Model Performance
+
+Training metrics after 10 epochs:
+- Initial Loss: 11.147
+- Final Loss: 3.527
+- Training Time: ~2 hours on NVIDIA V100
+
+## 🔧 Hyperparameters
+
+```python
+BATCH_SIZE = 32
+LEARNING_RATE = 1e-3
+CLIP = 1.0
+N_EPOCHS = 10
+ENC_EMB_DIM = 256
+DEC_EMB_DIM = 256
+ENC_HID_DIM = 512
+DEC_HID_DIM = 512
+```
+
+## 📚 Dataset
+
+Using the `loresiensis/corpus-en-es` dataset from Hugging Face Hub, which provides English-Spanish sentence pairs for training.
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper
+- Hugging Face for the transformers library and datasets
+- PyTorch team for the amazing deep learning framework
+
+---
+⭐️ If you found this project helpful, please consider giving it a star!