🧠 Machine-Translation

A multilingual machine translation system built from scratch using custom datasets, tokenizers, and models. The goal is to build a flexible and modular translation pipeline that can be easily extended to new languages and models.

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📁 Project Structure

├── Datasets/               # Preprocessed or raw language datasets  
├── Model/                  # Model architecture and training scripts  
├── Tokenizer/              # Tokenizer scripts or configs (e.g., SentencePiece, BPE)  
├── Create_Dataset.ipynb    # Jupyter notebook to create or preprocess datasets  
└── README.md               # Project documentation  

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🔧 Features

• Multilingual support (e.g., English ↔ French, English ↔ Spanish)
• Custom tokenizer training (SentencePiece or other)
• Clean dataset preprocessing pipeline
• Modular, customizable model architecture
• Easy integration and extension

📦 Requirements

• Python 3.8+
• PyTorch
• Hugging Face Transformers
• SentencePiece
• Jupyter Notebook

🚀 Getting Started

• Create Dataset Open Create_Dataset.ipynb and generate your parallel corpora.
• Tokenizer Training Use scripts under Tokenizer/ to train or load your tokenizer.
• Model Training Modify and run training scripts inside the Model/ directory.
• Evaluation (To-do) Evaluate translation quality using BLEU, ROUGE, or other metrics.

Install dependencies:

pip install -r requirements.txt

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🚀 Getting Started

1. Create Dataset
   Open Create_Dataset.ipynb and generate your parallel corpora.

2. Tokenizer Training
   Use scripts under Tokenizer/ to train or load your tokenizer.

3. Model Training
   Modify and run training scripts inside the Model/ directory.

4. Evaluation
   Evaluate translation quality using BLEU, ROUGE, or other metrics.

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🛠️ Future Enhancements

• Add CLI or Streamlit web demo
• Plug-and-play support for new language pairs
• Integration with external translation APIs for evaluation

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🤝 Contributing

Feel free to fork this project, raise issues, or submit PRs. Let’s build something awesome together.

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