Topics returned from model.transform differ from model.fit_transform

Hi,

When I run BERTopic using model.fit_transform on my dataset, it will return cluster numbers and titles that make sense for the input data. When I run the same string through the trained model using model.transform, it returns a different cluster number, though it is consistent. For example, something with "Mexican restaurant" might get assigned to topic 1 "restaurant_mexican restaurant_full service restaurant", but when I run the exact same record through model.transform, I'll get a different integer for the topic (corresponding to something totally different) and even a different probability - and these can even differ between runs of model.transform on the same trained model. Am I doing something wrong? This has been befuddling me. Note the topic numbers from model.get_topic_info and those returned from model.fit_transform do correspond with one another.

Perhaps related, but I saw a similar frustrating difference in the outputs of get_topic_info and the output when using calculate_probabilities=True, where the columns of the probability array did not correspond to the topic numbers from get_topic_info.

Reproduce the mismatch by comparing model.fit_transform, model.transform, get_topic_info, and calculate_probabilities on the same records. Trace how topic IDs and probability columns are produced and mapped; done means repeated transforms return stable assignments whose IDs and probability columns correspond to get_topic_info.