Implemented the Transformer model paper for language translation, supporting flexible source and target language selection to handle various language pairs, with a carefully designed architecture for efficient processing. Built an efficient data pipeline, including custom tokenizers for both source and target languages, and implemented batching to optimize training performance. Designed a robust validation process to evaluate the model using multiple performance metrics, ensuring comprehensive evaluation and monitoring throughout the training process.