Easy Machine Translation with Machine Learning and HuggingFace Transformers – MachineCurve

Transformers have significantly changed the way in which Natural Language Processing tasks can be performed. This architecture, which trumps the classic recurrent one – and even LSTM-based architectures in some cases, has been around since 2017 and is the process of being democratized today. And in fact, many tasks can use these developments: for example, text summarization, named entity recognition, sentiment analysis – they can all be successfully used with this type of model. In this tutorial, we will be looking at the task of machine translation. We'll first take a look at how Transformers can be used for this purpose, and that they effectively perform a sequence-to-sequence learning task.