Show the distribution of top k documents in each topic

Reference to https://github.com/MaartenGr/BERTopic/issues/93#:~:text=So%2C%20only%20set%20this%20to%20True%20if%20you%20have%20less%20than%20100.000%20documents., does it mean it will not compute probabilities when I have more than 100 documents (accurately, I have 40000+ documents) although setting calculate_probabilities=True?

My BERTopic model is:

umap_model = UMAP(n_neighbors=20, n_components=15, min_dist=0.0, metric='cosine', random_state=42)
cluster_model = KMeans(n_clusters=100, random_state=42)  # 100
vectorizer_model = CountVectorizer(stop_words="english")

ctfidf_model = ClassTfidfTransformer(
    seed_words=seed_words, 
    seed_multiplier=5
)

model = BERTopic(embedding_model='./paraphrase-MiniLM-L6-v2', 
                 umap_model=umap_model,
                 min_topic_size=50,
                 ctfidf_model=ctfidf_model,        
                 hdbscan_model=cluster_model,
                 vectorizer_model=vectorizer_model, 
                 calculate_probabilities=True
)

Then run this code:

topics, probabilities = model.fit_transform(abstracts)
print(probabilities)

The output is None.
Version:

# pip show bertopic
Name: bertopic
Version: 0.16.0
Summary: BERTopic performs topic Modeling with state-of-the-art transformer models.
Home-page: https://github.com/MaartenGr/BERTopic

I know how stressful it would be to compute all the probabilities. If the answer to the question at the beginning of this issue is yes， could you please add a parameter to let users set top k documents to show probabilities of most similar k documents for each topic?

Start at BERTopic's fit_transform entry point and reproduce the reported behavior with calculate_probabilities=True using the model configuration shown. Determine how a top-k probability result should be represented for 40,000+ documents, then verify that the requested output is produced without requiring the full probability matrix.