[Possible BUG] n_words parameter doesn't update y_label values

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Hello!

I'm trying to use your library to do simple topic modeling on the Yahoo answers topics dataset. Currently, I'm trying out the unsupervised approaches. When visualizing the final topics, the `n_words` parameter seems to only update the bars inside the plot but not the y label values as well.

Here is the code and the final image:
```
from umap import UMAP
from hdbscan import HDBSCAN
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
from bertopic.representation import MaximalMarginalRelevance
from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired
from datasets import load_dataset
import matplotlib.pyplot as plt

print("Downloading dataset . . .")
data = load_dataset("yahoo_answers_topics", split="test")
text = data.to_pandas()[["best_answer", "topic"]]
num_classes = len(set(text.topic.values))

print("Downloading model . . .")
embedding_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")

umap_model = UMAP(
n_neighbors=15, n_components=5, min_dist=0.0, metric="cosine"
)
kmeans_model = KMeans(n_clusters=num_classes)

representation_model = [
KeyBERTInspired(),
MaximalMarginalRelevance(diversity=0.5)
]

topic_model = BERTopic(
embedding_model=embedding_model,
umap_model=umap_model,
hdbscan_model=kmeans_model,
representation_model=representation_model,
verbose=True,
)

print("Finding topics . . .")
topics, probs = topic_model.fit_transform(text.best_answer.values)

fig = topic_model.visualize_barchart(
top_n_topics=num_classes,
n_words=10,
)
fig.write_image("topics_barchart.jpg")
```

Start at the BERTopic.visualize_barchart entry point used in the example and trace how n_words controls the bars and y-axis labels. Check whether the labels are built from a separate topic-word selection, then reproduce the reported figure with n_words=10. Done means the displayed labels and bars show the same number of words.

Written by the indexing model from the issue text.