text: Title: Calculating Probabilities on Zero-Shot Learning
  Body: ### Have you searched existing issues?  🔎
  
  - [X] I have searched and found no existing issues
  
  ### Desribe the bug
  
  Hi everyone, first of all I would like to thank @MaartenGr and all the contributors for this amazing project. 
  
  Recently I started looking at BERTopic as a method to classify some customer tickets into some categories defined into`zeroshot_topic_list` parameter. After fitting the model by calling `fit_transform` my goal was to look for each document, the probability of that document of belonging to all the topics (both predefined and generated ones).
  
  `probs` is `None` after `fit_transform`, as expected and mentioned at the end of this page https://maartengr.github.io/BERTopic/getting_started/zeroshot/zeroshot.html#example. Therefore, I then called `transform` to get the probabilities.
  
  Now I've got two questions I would like to answer:
  - is this one the right approach to get the probabilities?
  - most of the documents have almost the same (high) probability among all the topics. Does this mean the clustering didn't fit that much the data? What do you suggest?
  
  Again, I would like to thank you for your effort in advance and I look forward to contribute as well if needed.
  
  ### Reproduction
  
  ```python
  topic_model = BERTopic(
    calculate_probabilities = True,
    vectorizer_model = CountVectorizer(stop_words=default_stopwords + custom_stopwords),
    ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True),
    embedding_model = sentence_model,
    min_topic_size = 50,
    zeroshot_topic_list = taxonomy_list,
    zeroshot_min_similarity = .80,
    representation_model = KeyBERTInspired(),
    verbose = True,
  )
  
  topics, _ = topic_model.fit_transform(global_ticket_descriptions, embeddings=embeddings)
  _, probs = topic_model.transform(global_ticket_descriptions, embeddings=embeddings)
  print(probs)
  ```
  
  The output is:
  ```
  2024-07-09 14:48:15,691 - BERTopic - Predicting topic assignments through cosine similarity of topic and document embeddings.
  [[0.8463203  0.8880229  0.8636132  ... 0.8376566  0.8344382  0.82069004]
   [0.92492086 0.8977871  0.91309047 ... 0.9031642  0.90693504 0.9101905 ]
   [0.9009018  0.9072951  0.91581297 ... 0.9002963  0.8903491  0.8731015 ]
   ...
   [0.85506856 0.9055494  0.8743948  ... 0.8490986  0.8607102  0.8375366 ]
   [0.8783543  0.88571143 0.8983458  ... 0.8795974  0.87251174 0.8653137 ]
   [0.8480984  0.8751351  0.8680049  ... 0.8383051  0.8333502  0.8458183 ]]
  ```
  
  
  ### BERTopic Version
  
  0.16.2