你好，

我已经按照指南中的说明进行了操作 - https://colab.research.google.com/drive/1W7aEdDPxC29jP99GGZphUlqjMFFVKtBC?usp=sharing#scrollTo=oGQnu6lO9SrE
特别是关于手动实现BERTopic的部分。在执行过程中，我为BERTopic指定了calculate_probabilities=True。然而，在使用以下代码片段进行模型拟合后，预期的概率没有返回。

请问您能否提供一些见解或解决方案，以修改代码，使其成功返回hdbscan_model输出中的概率矩阵？

感谢您的帮助。

from cuml.cluster import HDBSCAN
from sentence_transformers import SentenceTransformer

from bertopic import BERTopic
from bertopic.cluster import BaseCluster
from bertopic.representation import KeyBERTInspired

class Dimensionality:
  """ Use this for pre-calculated reduced embeddings """
  def __init__(self, reduced_embeddings):
    self.reduced_embeddings = reduced_embeddings

  def fit(self, X):
    return self

  def transform(self, X):
    return self.reduced_embeddings

# Prepare sub-models
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
umap_model = Dimensionality(reduced_embeddings)
hdbscan_model = BaseCluster()
vectorizer_model = CountVectorizer(vocabulary=vocab, stop_words="english")
representation_model = KeyBERTInspired()

# Fit BERTopic without actually performing any clustering
topic_model= BERTopic(
        embedding_model=embedding_model,
        umap_model=umap_model,
        hdbscan_model=hdbscan_model,
        vectorizer_model=vectorizer_model,
        representation_model=representation_model,
      calculate_probabilities= True,
        verbose=True
).fit(texts, embeddings=embeddings, y=clusters)