Hey Shaun! Sorry I’m just replying– holiday study break 
I was able to run regression, though I am not positive that it worked. The loss was decreasing, but my predictions never got very good. What I don’t know is whether that is due to insufficient data (I’m using Twitter text to predict likes, which is a very incomplete prediction) or if it’s due to not having my data, model and loss function set up correctly.
I am not sure how the output of a model can be size 0, that is a good question! Here’s how I got that number: after creating the text_classifier_learner with the factory method, I used learner.model to examine the layers, and the final layer listed as in_features=50, out_features=0. You’re right– having an output of zero doesn’t seem possible, does it?
I originally edited the PoolingLinearClassifier's mod_layers to hard code the final layer, but I changed my approach to customize the text_classifier_learner, changing the line that reads this:
vocab_size, n_class = len(data.vocab.itos), data.c
to this: vocab_size, n_class = len(data.vocab.itos), 1
From my digging it looked like establishing n_class here then filters down into text_rnn_classifier to hard code the final layer output size. I didn’t do anything to the PoolingLinearClassifier in this case. That said, it’s possible I am not yet confident I’ve configured regression correctly! But it runs and my loss goes down, albeit just a bit.