(Zero-shot Topic Modeling) TypeError: object of type 'numpy.float64' has no len()

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Hello! I'm currently working on my project, and I have a specific NLP task using BERTopic - Zero-shot Topic Modeling. Unfortunately, a bug exists when I try to form the model.

Here is my model formation:

```python
embedding_model_en = SentenceTransformer("all-MiniLM-L6-v2")
embeddings_en = embedding_model_en.encode(df_comment_1['text_en'], show_progress_bar=True)
umap_model_en = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42)
hdbscan_model_en = HDBSCAN(min_cluster_size=40, metric='euclidean', cluster_selection_method='eom',` prediction_data=True)
vectorizer_model_en = CountVectorizer(min_df=2, ngram_range=(1, 2))
zeroshot_topic_list = ["good", "bad"]
keybert_model = KeyBERTInspired()
mmr_model = MaximalMarginalRelevance(diversity=0.3)
representation_model_en = {
"KeyBERT": keybert_model,
"MMR": mmr_model,
}
topic_model_en = BERTopic(
embedding_model=embedding_model_en,
umap_model=umap_model_en,
hdbscan_model=hdbscan_model_en,
vectorizer_model=vectorizer_model_en,
representation_model=representation_model_en,
zeroshot_topic_list=zeroshot_topic_list,
zeroshot_min_similarity=0.5,
verbose=True,
nr_topics=50
)
```

And when I run:
`topics_en, probs_en = topic_model_en.fit_transform(df_comment_1['text_en'], embeddings_en)`

I get the following error:
```
TypeError Traceback (most recent call last)
[](https://localhost:8080/#) in ()
11 )
12
---> 13 topics_en, probs_en = topic_model_en.fit_transform(df_comment_1['text_en'], embeddings_en)
14 topic_model_en.save('my_model_en_22', serialization="safetensors")
15 topic_model_en.get_topic_info()

7 frames
[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in fit_transform(self, documents, embeddings, images, y)
446 # Combine Zero-shot with outliers
447 if self._is_zeroshot() and len(documents) != len(doc_ids):
--> 448 predictions = self._combine_zeroshot_topics(documents, assigned_documents, assigned_embeddings)
449
450 return predictions, self.probabilities_

[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in _combine_zeroshot_topics(self, documents, assigned_documents, embeddings)
3619 empty_dimensionality_model = BaseDimensionalityReduction()
3620 empty_cluster_model = BaseCluster()
-> 3621 zeroshot_model = BERTopic(
3622 n_gram_range=self.n_gram_range,
3623 low_memory=self.low_memory,

[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in fit(self, documents, embeddings, images, y)
314 ```
315 """
--> 316 self.fit_transform(documents=documents, embeddings=embeddings, y=y, images=images)
317 return self
318

[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in fit_transform(self, documents, embeddings, images, y)
431 else:
432 # Extract topics by calculating c-TF-IDF
--> 433 self._extract_topics(documents, embeddings=embeddings, verbose=self.verbose)
434
435 # Reduce topics

[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in _extract_topics(self, documents, embeddings, mappings, verbose)
3784 logger.info("Representation - Extracting topics from clusters using representation models.")
3785 documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
-> 3786 self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
3787 self.topic_representations_ = self._extract_words_per_topic(words, documents)
3788 self._create_topic_vectors(documents=documents, embeddings=embeddings, mappings=mappings)

[/usr/local/lib/python3.10/dist-packages/bertopic/_bertopic.py](https://localhost:8080/#) in _c_tf_idf(self, documents_per_topic, fit, partial_fit)
4006
4007 if fit:
-> 4008 self.ctfidf_model = self.ctfidf_model.fit(X, multiplier=multiplier)
4009
4010 c_tf_idf = self.ctfidf_model.transform(X)

[/usr/local/lib/python3.10/dist-packages/bertopic/vectorizers/_ctfidf.py](https://localhost:8080/#) in fit(self, X, multiplier)
86 idf = idf * multiplier
87
---> 88 self._idf_diag = sp.diags(idf, offsets=0,
89 shape=(n_features, n_features),
90 format='csr',

[/usr/local/lib/python3.10/dist-packages/scipy/sparse/_construct.py](https://localhost:8080/#) in diags(diagonals, offsets, shape, format, dtype)
146 if isscalarlike(offsets):
147 # now check that there's actually only one diagonal
--> 148 if len(diagonals) == 0 or isscalarlike(diagonals[0]):
149 diagonals = [np.atleast_1d(diagonals)]
150 else:

TypeError: object of type 'numpy.float64' has no len()
```
How can I fix that error?
Thank you.

Reproduce the zero-shot configuration with fit_transform, then read _bertopic.py around fit_transform and _combine_zeroshot_topics, followed by vectorizers/_ctfidf.py around fit. Trace the value passed to scipy.sparse.diags and compare it with the zero-shot documents and embeddings. Done means the reported configuration completes without the numpy.float64 len() TypeError.

Written by the indexing model from the issue text.