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Question about combining models #37
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Clarifying: I've been playing around with "combine" and have gotten it to work, but I'm curious - when it "combines" the models with weighting, is it just using those weights to choose which corpus the words come from, or do the corpuses actually mix? (For example, if I trained a model on the KJV and Moby Dick, could I get sentences that combine both texts? Or would I just get the right fraction of sentences that come from each text?) |
Yep!
The latter. The corpuses are, effectively, mixed.
Yep! That's what should happen. (Would be curious to see the output.)
Nope! There's currently no way to do that with |
Thanks so much! That's what I was guessing - I think the length difference between the texts was just giving me lots more Bible words, but I wanted to make sure that it wasn't a weighting mistake. KJV/Moby Dick didn't produce anything terribly interesting on the couple of test runs I did (I'm currently just setting up the skeleton of my project), but I got some pretty fun results with Moby Dick + Pride and Prejudice:
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Love those examples. Thanks for sharing! |
If I do markovify.combine() with no weighting, is it effectively the same as training one model on the texts of all the combined models? Asking because I'd like to train a model on a lot of text files, and it works out easier to create a bunch of different ones and then combine them, as long as that works the way I'm expecting it to.
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