Zeroshot Topic Modeling With no Embedding Model

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Hello @MaartenGr and thanks for the awesome bertopic library! I want to perform zeroshot topic modeling with no embedding model. I have used an external model to get embeddings of documents and zeroshot topic list. I have no access to that embedding model anymore.

Is it possible to run something like this without embedding model?

```
zeroshot_topic_list_embeddings = np.random.rand(len(zeroshot_topic_list), 1024).astype(np.float32)
document_embeddings = np.random.rand(len(docs), 1024).astype(np.float32)

sim = 0.8
ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)
representation_model = KeyBERTInspired(top_n_words=200)
topic_model = BERTopic(
top_n_words = 20,
ctfidf_model=ctfidf_model,
verbose=True,
calculate_probabilities = True,
embedding_model=None,
min_topic_size=200,
zeroshot_topic_list=zeroshot_topic_list,
zeroshot_min_similarity=sim,
representation_model=representation_model
)
topics, probs = topic_model.fit_transform(docs,document_embeddings)
topics, probs = topic_model.transform(docs,document_embeddings)

freq = topic_model.get_topic_info()
```

I think somewhere in the code Bertopic is still trying to use the embedding model

Start with the BERTopic fit_transform and transform entry points shown in the example, using supplied document embeddings with embedding_model=None and zeroshot_topic_list. Trace the zeroshot path to identify where an embedding model is still required; done means both calls complete without access to the original embedding model.

Written by the indexing model from the issue text.