Updating and Pushing a BERTopic Model with New Documents to Hugging Face Hub still shows old no of training document

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### Desribe the bug

I have been using BERTopic for topic modelling and recently needed to update my existing BERTopic model with new documents. I want to push the updated model to the Hugging Face Hub, ensuring that it reflects the new number of documents and topics.

Here’s what I’ve done so far:

- Loaded my existing BERTopic model:
- Added new documents and their embeddings:
- Updated the model with new documents:

```
`new_topics, new_probs = topic_model.transform(lemmatized_docs, embeddings)`
```

- Saved the updated model using safetensors:
- Pushed the updated model to Hugging Face Hub:

Despite following these steps, I still see the old number of training documents in the repository on the Hugging Face Hub. How can I ensure that the updated model reflects the new number of training and topics?

Any help or guidance on this would be greatly appreciated!

### Reproduction

```python
from bertopic import BERTopic

# Load your existing BERTopic model
topic_model= BERTopic.load("shantanudave/BERTopic_ArXiv",embedding_model="sentence-transformers/all-MiniLM-L6-v2")

new_topics, new_probs = topic_model.transform(lemmatized_docs, embeddings)

new_model_name = "BERTopic_v2"

# Save the updated model locally using safetensors

embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
topic_model.save(new_model_name, serialization="safetensors", save_ctfidf=True, save_embedding_model=embedding_model)

from huggingface_hub import login

# Authenticate with Hugging Face
login(token="your_hugging_face_token")

# Push the updated model to Hugging Face Hub
topic_model.push_to_hf_hub(
repo_id=f"shantanudave/{new_model_name}",
serialization="safetensors",
save_ctfidf=True,
save_embedding_model=embedding_model
)

```

### BERTopic Version

pip install -U bertopic

Start with the reproduction using BERTopic.load, transform, save, and push_to_hf_hub. Inspect how model metadata is written during saving and uploading, then verify whether the Hub artifact reflects the expected training-document and topic counts. Done means the behavior is explained and, if it is a bug, covered by a reproducible regression test.

Written by the indexing model from the issue text.