subclassing markovify.Text to allow for different types of 'sentences'

hi and thx for yr great library.

i made a cli program to run it on my own texts.

i'm trying to add a subclass to it that enables me to feed it sentences that dont begin with initial capital letters and might begin with stars, bullets, etc. i made a subclass (modeled on your NewlineText) to modify the regexes in split_into_sentences(), changing the lookahead search that mandates an initial capital letter after sentence end (splitters.py, line 45) to read r"\s+(?=[-•\w‘’“”'*\|/~\",])",, and added a few more punctuation marks to the previous regexes (hypen, ellipses/triple periods).

it works if i manually generate a corpus and markov model from one of my texts, but not if i run my program using the subclass. one "sentence" will have a period in the middle of it and will continue printing text after it.

so i wanted to ask if there anything in the way that sentences are made from the markov model that would affect these modified regexes or disregard them? and is there a better way to go about modifying sentence endings than messing with split_into_sentences()?

[sorry if its obvious in the code. i'm very much a novice with programming.]

Start in splitters.py at split_into_sentences(), comparing the proposed regex changes with the existing NewlineText subclass. Trace how the resulting sentences are passed into model generation and identify where a period can remain inside one sentence. Done means the subclass handles the described sentence starts and endings consistently when used through the normal program path.