LSTM_Model

English-Norwegian LSTM Translator Project Summary This project focuses on building an LSTM-based Seq2Seq model for translating text between English and Norwegian. The model uses a neural network with LSTMs for both encoding and decoding sentences. It provides a lightweight yet effective solution for language translation without relying on attention mechanisms.

Key Features Model Design:

Encoder-Decoder structure with LSTMs. Designed for translating from English to Norwegian. Functionality:

Preprocessing and tokenization of English-Norwegian text pairs. Tracks model performance through training and validation loss. BLEU and ChrF scoring for translation quality assessment. Visualization and Outputs:

Training and validation loss curves for model performance monitoring. Exportable model checkpoints for future reuse or sharing. Deployment Ready:

Can be hosted on HuggingFace for easy access and deployment. Included Files LSTM_Model_Translator_NithinYadav.ipynb: Contains all steps including data preparation, model implementation, training, evaluation, and result visualization. How to Use Prepare the Dataset:

Use a parallel English-Norwegian dataset with sentence pairs. Ensure preprocessing steps like tokenization, padding, and vocabulary generation are completed (handled in the notebook). Run the Notebook:

Open the provided Jupyter notebook and execute cells step by step. Modify hyperparameters (e.g., epochs, batch size) as needed. Training the Model:

Train the Seq2Seq model and observe loss curves to track progress. Save trained checkpoints for later usage or deployment. Evaluate the Model:

Use BLEU and ChrF scores to measure translation accuracy. Test the model on new text for validation. Project Results Training & Validation Loss: The model quickly converges with near-zero losses, indicating strong performance. Translation Quality: High BLEU and ChrF scores, demonstrating accurate and fluent translations. Requirements Python 3.8 or higher Libraries: TensorFlow, NumPy, Matplotlib, and others (listed in the notebook) Potential Improvements Implement attention mechanisms for enhanced translation quality. Train on a larger, more diverse dataset for better generalization. Extend the model to support multiple language pairs. About the Author This project was developed by Nithin Yadav as part of a natural language processing task. Contributions, feedback, and ideas are welcome!

License This project is distributed under the MIT License. You are free to use, modify, and share the code for any purpose.