This project demonstrates English-to-Hindi machine translation using Long Short-Term Memory (LSTM) neural networks. Implemented entirely in a Jupyter Notebook, it leverages deep learning techniques to translate sentences from English to Hindi.
Machine translation is a key application of NLP, allowing automatic translation between languages. This project uses an LSTM-based sequence-to-sequence model to learn translations from English to Hindi, with data sourced from Kaggle.
- Source: The dataset is sourced from Kaggle and contains parallel English-Hindi sentences.
- Dataset Link: Kaggle Dataset for English-Hindi Translation (Please replace this link with the actual URL of the dataset you used).
- Data Format: Each entry in the dataset includes an English sentence and its corresponding Hindi translation.
To access the dataset:
- Download it from Kaggle.
- Place the dataset file in the same directory as the notebook.
- Jupyter Notebook: Run the project in a Jupyter environment.
- Python Libraries:
tensorflow
for deep learning model implementation.numpy
for numerical operations.pandas
for data handling.matplotlib
for visualizations.
You can install the required libraries with:
pip install tensorflow numpy pandas matplotlib
### How to Run
1. **Open the Notebook**: Launch Jupyter Notebook and open `English_Hindi_Translation.ipynb`.
2. **Run Each Cell**: Execute each cell in the notebook sequentially. The notebook handles data loading, preprocessing, model training, and evaluation.
3. **Translate Sentences**: The notebook includes a section to test the trained model by translating sample English sentences into Hindi.
### Project Structure
- **`English_Hindi_Translation.ipynb`**: The Jupyter Notebook containing the full implementation:
- **Data Preprocessing**: Tokenizes English and Hindi sentences and pads sequences.
- **Model Definition**: Builds an encoder-decoder LSTM model.
- **Training**: Trains the model using the prepared dataset.
- **Evaluation**: Tests the model’s translation accuracy and demonstrates sample translations.
### Example Output
Example translation from the model:
- **Input (English)**: "Good morning."
- **Output (Hindi)**: "सुप्रभात।"
### Future Improvements
- **Attention Mechanism**: Enhance the model with attention to improve translation accuracy.
- **Bidirectional LSTMs**: Use bidirectional layers for richer context understanding.
- **Hyperparameter Tuning**: Experiment with different learning rates, batch sizes, and sequence lengths for better performance.
### References
- [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215)
- [TensorFlow Neural Machine Translation Tutorial](https://www.tensorflow.org/text/tutorials/nmt_with_attention)
- [Kaggle Dataset](https://www.kaggle.com/datasets) *(Replace with the specific dataset link)*