When I started this project, one of the ideas I had was to somehow combine the subreddit-models I'd created with other models that were fine-tuned on non-reddit text corpora, with the goal of generating submissions/comments that are written in distinct "styles". For example, I thought it would be cool to see a submission to /r/shortscarystories in the style of H.P. Lovecraft, or to see an /r/the_donald thread in which every comment is written in the style of G.K. Chesterton.

I'd experimented with a few ways of doing this that ended up not working, but last week I found a method that (IMO) actually gives decent results.

As you'll see, it's definitely not perfect. There's clearly some loss of coherency for these hybrid models as compared to the normal subreddit-specific ones, especially for pairings where the subreddit content is very different from the non-reddit style it's being combined with. Also, the meta-data (specifically the commentIDs) sometimes leak into the generated comment-bodies, since the non-reddit models didn't learn the meta-data format I'm using and mistake it for just normal text. But overall I was still happy with how they turned out, and I think there are ocassionally some really interesting/funny combinations.

For now, I've trained separate models in each of the following styles (I plan on adding more later):

• G.K. Chesterton (all his published non-fiction)
• H.P. Lovecraft (all published fiction, non-fiction, poetry)
• Marcel Proust (full text of In Search of Lost Time, Moncrieff translation)
• The King James Bible (Old + New Testament)

Over the next 24 hours, I'll be releasing only threads generated by these hybrid models. These will be posted by the usual subreddit-bot accounts, but they can be differentiated from normal threads by the tags, which will be "hybrid:chesterton", "hybrid:lovecraft", "hybrid:proust", or "hybrid:bible" instead of the subreddit name. Note that these are slightly cherry-picked for quality, since I actually generated too many threads to post in one day.

After 24 hours I'll go back to posting normal threads for the rest of the week, though I may post some more hybrids next weekend.

Implementation details, if you're interested

I ended up using a slightly modified version of the LM class from this repository, which relies on the HuggingFace pytorch implementation of GPT-2.

Specifically, my LM class takes in two paths (and builds two separate models) rather than one. Then, at each step it generates two (one for each model) probability estimates for every possible token. Referring to those lists as subredditModelProbs and styleModelProbs, I then compute the list of "combined" probability estimates (combinedModelProbs) as follows:

rawAvgModelProbs = sqrt((1-w)*subredditModelProbs^2 + w*styleModelProbs^2)
rawAvgModelProbs = rawAvgModelProbs / rawAvgModelProbs.sum()
for i in tokens:
   if(i in specialTokenlist):
        #specialTokenList is a list consisting of tokens I'm using to represent reddit metadata, as well as newline tokens
        #I have to trust the subreddit-model probabilities for the metadata tokens, since the style model's training set didn't have any reddit metadata in it and therefore didn't learn it
        #I also use purely the subreddit-model probability for new-line tokens, since otherwise the submission titles (and many of the comments) will tend to go on for far too long
        combinedModelProbs[i] = subredditModelProbs[i]
   else:
        #For non-metadata tokens (ie nearly all of the tokens), I'm essentially just averaging the probabilities and then re-weighting so everything sums to 1.
        combinedModelProbs[i] = rawAvgModelProbs[i]* (1-subredditModelProbs[specialTokenList].sum())/(1-rawAvgModelProbs[specialTokenList].sum())

where w is a number between 0 and 1 representing the weight on the style model as compared to the subreddit model. The samples I'm posting now were generated using a variety of different values for w, since I'm still trying to see exactly which works best. I initially tried using equal weights (w=0.5), but it seemed like the non-reddit style tended to overwhelm the subreddit-specific model, and it was sometimes hard to tell from the comments alone which subreddit-model was being used. So I'm currently leaning towards using 0.3 for most of the pairings.

Using these combined probabilities I then generate the samples in the normal way, with topk=40.

I'd be interested to hear if any of you have tried combining two models in a similar way, or if you know of a better alternative method.

EDIT: It's back to the normal posting schedule now. You can see all the hybrid posts here