Calculating Probabilities on Zero-Shot Learning

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### 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

The report exercises BERTopic.fit_transform and transform with calculate_probabilities=True and zeroshot_topic_list; begin by tracing those entry points and the zero-shot probability handling. No file or test is named, and completion criteria are not defined, so first confirm whether this is a bug requiring a code or test change or documentation explaining the observed probabilities.

Written by the indexing model from the issue text.