This is a project to build a machine translation model using the Transformer model. The model is trained on the English-Vietnamese dataset. The Transformer model is a deep learning model that has been shown to be very effective in machine translation tasks. The model is trained using the PyTorch framework.
The dataset used for training the model is the English-Vietnamese dataset. The dataset contains pairs of English and Vietnamese sentences. The dataset is preprocessed and tokenized before being used for training.
The model architecture used for the machine translation model is the Transformer model. The Transformer model is a deep learning model that has been shown to be very effective in machine translation tasks. The model consists of an encoder and a decoder, each of which is made up of multiple layers of self-attention and feedforward neural networks.
The model is trained using the PyTorch framework. The model is trained on the English-Vietnamese dataset using the Adam optimizer and the cross-entropy loss function. The model is trained for a number of epochs until the loss converges.
The model is evaluated on a separate test set of English-Vietnamese sentence pairs. The model is evaluated using the BLEU score, which is a metric that measures the quality of machine translation models. The model is also evaluated qualitatively by examining the translations produced by the model.
In this project, we built a machine translation model using the Transformer model. The model was trained on the English-Vietnamese dataset and evaluated using the BLEU score. The model was shown to be effective in translating English sentences to Vietnamese. The model can be further improved by training on a larger dataset and fine-tuning the hyperparameters.