Different number of topics for different training runs on the same dataset

Hi,

I am facing issue. If I train bertopic on a same dataset multiple times, I am getting different number of topics .

As per the discussion in this thread: https://github.com/MaartenGr/BERTopic/issues/461 . I have tried below two ways . But neither resolved the issue. In both ways running code multiple times is giving different number of topics:-

I am running programme on amazon sagemaker notebook instance.

docs : this my list of documents used in training .

(1)
from sklearn.feature_extraction.text import CountVectorizer
from umap import UMAP
from bertopic import BERTopic

vectorizer_model = CountVectorizer(ngram_range=(2, 3), stop_words='english')
umap_model = UMAP(random_state=42)
topic_model = BERTopic(vectorizer_model=vectorizer_model , umap_model=umap_model)
topics, probabilities = topic_model.fit_transform(docs)

(2)
from sklearn.feature_extraction.text import CountVectorizer
from umap import UMAP
from bertopic import BERTopic

vectorizer_model = CountVectorizer(ngram_range=(2, 3), stop_words='english')
umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42)
topic_model = BERTopic(vectorizer_model=vectorizer_model , umap_model=umap_model)
topics, probabilities = topic_model.fit_transform(docs)

Thank you!

Reproduce the repeated BERTopic fit_transform runs in the SageMaker notebook using the two CountVectorizer and UMAP configurations shown. Read the linked issue #461 alongside the UMAP random_state settings and compare the resulting topic counts across runs. Done means identifying and documenting the remaining source of nondeterminism or confirming the expected behavior.