Array mismatch when try to fit new data

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Hello! I'm very thankful for your tool and library it's so much awesome!

I should apologise for my basic (stupid) question in advance, however i can't solve it.

First, i'm making model with .fit on pandas DF with 20293 rows (texts)

```
from bertopic.representation import MaximalMarginalRelevance
representation_model = MaximalMarginalRelevance(diversity=0.4)

topic_model = BERTopic(
embedding_model=sentence_model,
umap_model=umap_model,
hdbscan_model=hdbscan_model,
vectorizer_model=vectorizer_model,
ctfidf_model=ctfidf_model,
representation_model=representation_model,
top_n_words=6,
verbose=True,
)

#topics, probs = topic_model.fit_transform(data['filtered_words'],embeddings=embeddings)
```

Then i make auto nr to 15 topics and reduce outliers with c-TF-IDF strategy.

I have resulting df with all document assigned to topics:
`result=topic_model.get_document_info(data['filtered_words'])
result_df = data.join(result, how='inner')`

Then i save model to use it with new data:
`topic_model.save("testmodel", serialization="pytorch", save_ctfidf=True, save_embedding_model=sentence_model)`

So now i'm trying to get document info only for new data
```
sentence_model = SentenceTransformer("cointegrated/LaBSE-en-ru")
embeddings = sentence_model.encode(data['filtered_words'], show_progress_bar=True)
topic_model11 = BERTopic.load("testmodel")
new_topics, new_probs = topic_model11.transform(data['filtered_words'],embeddings=embeddings) #//here's new data is used
new_topics
document_info = topic_model11.get_document_info(combined_df['filtered_words']) #// here's combined df is just new data for example of problem

```
And error
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[188], line 1
----> 1 document_info = topic_model11.get_document_info(combined_df['filtered_words'])

File ~\anaconda3\envs\test_env3\lib\site-packages\bertopic\_bertopic.py:1633, in BERTopic.get_document_info(self, docs, df, metadata)
1631 document_info["Topic"] = self.topics_
1632 else:
-> 1633 document_info = pd.DataFrame({"Document": docs, "Topic": self.topics_})
1635 # Add topic info through `.get_topic_info()`
1636 topic_info = self.get_topic_info().drop("Count", axis=1)

File ~\anaconda3\envs\test_env3\lib\site-packages\pandas\core\frame.py:733, in DataFrame.__init__(self, data, index, columns, dtype, copy)
727 mgr = self._init_mgr(
728 data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
729 )
731 elif isinstance(data, dict):
732 # GH#38939 de facto copy defaults to False only in non-dict cases
--> 733 mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
734 elif isinstance(data, ma.MaskedArray):
735 from numpy.ma import mrecords

File ~\anaconda3\envs\test_env3\lib\site-packages\pandas\core\internals\construction.py:503, in dict_to_mgr(data, index, columns, dtype, typ, copy)
499 else:
500 # dtype check to exclude e.g. range objects, scalars
501 arrays = [x.copy() if hasattr(x, "dtype") else x for x in arrays]
--> 503 return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)

File ~\anaconda3\envs\test_env3\lib\site-packages\pandas\core\internals\construction.py:114, in arrays_to_mgr(arrays, columns, index, dtype, verify_integrity, typ, consolidate)
111 if verify_integrity:
112 # figure out the index, if necessary
113 if index is None:
--> 114 index = _extract_index(arrays)
115 else:
116 index = ensure_index(index)

File ~\anaconda3\envs\test_env3\lib\site-packages\pandas\core\internals\construction.py:690, in _extract_index(data)
685 if lengths[0] != len(index):
686 msg = (
687 f"array length {lengths[0]} does not match index "
688 f"length {len(index)}"
689 )
--> 690 raise ValueError(msg)
691 else:
692 index = default_index(lengths[0])

ValueError: array length 20293 does not match index length 111
```

I figured it out that if i concat new data with old data and then use .get_document_info, it's ok except there's somehow one excess row which i delete from new data so that would not lead to mismatch error. Let me know if you need more info or my whole data/ code. Thanks!

[piplist_toshare.txt](https://github.com/user-attachments/files/15595779/piplist_toshare.txt)

Start in bertopic/_bertopic.py at get_document_info, using the traceback lines around construction of the Document and Topic columns. Reproduce the sequence with fit, save/load, transform, and get_document_info on the new data, then compare the document and topic lengths. Done means the new-data call no longer raises a length mismatch and reports the correct topics.

Written by the indexing model from the issue text.