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How to Train and Test an AI Language Translation System

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19 Apr 2021CPOL3 min read
In this article, we’ll train and test the model we created in the previous entry in the series.
Here we'll create a Keras tokenizer that will build an internal vocabulary out of the words found in the parallel corpus, use a Jupyter notebook to train and test our model, and try running our model with self-attention enabled.


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This article is part of the series 'Using Deep Learning for Automatic Translation View All


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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