I wish the tuts on PyTorch started off with a busiiess use case but they never seem to do that which makes it extremely difficult to find the correct tutorial.
I have a dataset with two columns, one contains a paragraph of text and the other a label (intent).  The dataset contains five possible labels.  All the data is pre-tagged.  I would like a model to take the inputs as is (the labels) and have it learn the contextual similarities based on the paragraphs and their pretagged label.
I created an NLP model to support this data structure in Apache OpenNLP many years agao but want to convert it into a PyTorch/Python model.  Is there a tutorial someone can recommend that walks a newbie through this type of scenario?