W.P. McNeill:

I want to train a Textcategorizer model with the following (text, label) pairs.

Label COLOR:

• The door is brown.
• The barn is red.
• The flower is yellow.

Label ANIMAL:

• The horse is running.
• The fish is jumping.
• The chicken is asleep.

I am copying the example code in the documentation for TextCategorizer.

textcat = TextCategorizer(nlp.vocab)
losses = {}
optimizer = nlp.begin_training()
textcat.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)

The doc variables will presumably be just nlp("The door is brown.") and so on. What should be in gold1 and gold2? I'm guessing they should be GoldParse objects, but I don't see how you represent text categorization information in those.