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Working with Transformer Models for Machine Translation

Stefania Cristina continues a series on transformer models. First up is plotting loss curves:

We have previously seen how to train the Transformer model for neural machine translation. Before moving on to inferencing the trained model, let us first explore how to modify the training code slightly, in order to be able to plot the training and validation loss curves that can be generated during the learning process. 

The training and validation loss values provide important pieces of information, because they allow us to have a better insight on how the learning performance is changing over the number of epochs, and help us diagnose any problems with learning that can lead to an underfit or an overfit model. They will also inform us about the epoch at which to use the trained model weights at the inferencing stage.

Then we get to try it out:

We have seen how to train the Transformer model on a dataset of English and German sentence pairs, as well as how to plot the training and validation loss curves in order to diagnose the model’s learning performance and decide at which epoch to inference the trained model. We are now ready to inference the trained Transformer model for the purpose of translating an input sentence.

In this tutorial, you will discover how to inference the trained Transformer model for neural machine translation. 

Click through for the results and to see exactly why there’s so much computational effort dumped into high-end trained models.