partial_fit throws warning and inf scores

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### Desribe the bug

running partial_fit starts to throw error after ~100 iterations
```
~/.venv/lib/python3.12/site-packages/bertopic/vectorizers/_ctfidf.py:84 RuntimeWarning: overflow encountered in divide
idf = np.log((avg_nr_samples / df) + 1)
```

### Reproduction

I'm on an M3 MacBook Pro
Python 3.12.4
scikit-learn 1.5.1
bertopic 0.16.3
numpy 1.26.4
scipy 1.14.0

- "df" is a pandas dataframe with a column "message", where each row is a string (in my use case, between 8 and 50 characters)
- "embeddings" is an np ndarray of shape [len(message), 384], of embeddings of the messages above. Embedded with the below embedding model:
```python
embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
```

here is a slightly modified version of the partial_fit example from the docs:
```python
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer

# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)

topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model
)

batch_size = 128
for idx in range(0, len(df), batch_size):
docs = df["message"].iloc[idx:idx+batch_size]
embeds = embeddings[idx:idx+batch_size]
topic_model.partial_fit(list(docs.astype('string')), embeds)
```

### BERTopic Version

0.16.3

Start with bertopic/vectorizers/_ctfidf.py at the reported line 84 and trace how partial_fit updates the online vectorizer. Run the supplied partial_fit example with the listed versions and inspect when the overflow and infinite scores begin. Done means the repeated updates no longer emit the warning and produce finite scores.

Written by the indexing model from the issue text.