`seed_topic_list` throwing error related to inhomogeneous shape after 1 dimensions

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I am using the following code to use `seed_topic_list`

```{python}
embedding_model = 'all-mpnet-base-v2'
word_list = ["Bullion",'market','price','commodity',"precious", "metal",'gilt','carat','aurum','world', 'gold', 'council','mine','mining','bitcoin','forecast','bank','liquidity','ingot','stocks','delivery','settlement','ETF']
word_lists = [[word.lower()] for word in word_list]

model = BERTopic(
verbose=True,
min_topic_size=5,
language="english",
seed_topic_list = word_list,
embedding_model = SentenceTransformer(embedding_model)
)
topics, probs = model.fit_transform(df['news_article'])
```
I am getting the following error!


Can somebody tell me, what is the correct way to use `seed_topic_list`?

Start at the BERTopic constructor's seed_topic_list argument and the subsequent fit_transform call, comparing the supplied word_list with the expected input shape. Use the traceback shown in the issue to identify the failing operation, then verify that the corrected input allows fit_transform to complete successfully.

Written by the indexing model from the issue text.