Guided Modeling: Problem with seed_topic_list

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Hello,

I've been having problems with performing a guided topic approach. The error I have been receiving is as such:
```
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
```

The part of my code is as follows:
```python3
from sklearn.feature_extraction.text import CountVectorizer
from bertopic.representation import KeyBERTInspired, PartOfSpeech, MaximalMarginalRelevance

# Guided Model
seed_topic_list = [['hacking', 'hackers', 'hacked', 'lost', 'account'],
['data', 'leak', 'permissions', 'unauthorised', 'privacy'],
['bugs', 'crash', 'ddos', 'server', 'virus'],
['username', 'password', 'name', 'credit', 'email'],
['oculus', 'htc', 'windows', 'mac', 'meta']]

g_main_representation_model = KeyBERTInspired()
g_aspect_representation_model1 = PartOfSpeech("en_core_web_sm")
g_aspect_representation_model2 = [KeyBERTInspired(top_n_words=30), MaximalMarginalRelevance(diversity=.5)]

g_representation_model = {
"Main": g_main_representation_model,
"Aspect1": g_aspect_representation_model1,
"Aspect2": g_aspect_representation_model2
}

g_vectorizer_model = CountVectorizer(min_df=5, stop_words = 'english')
g_topic_mdl_rec = BERTopic(nr_topics = 'auto', vectorizer_model = g_vectorizer_model,
representation_model = g_representation_model, seed_topic_list=seed_topic_list)
g_topics_rec, g_ini_probs_rec = g_topic_mdl_rec.fit_transform(rec_room_reviews)
```

The solutions I have tried:

- #1602 I tried to downgrade numba to version 0.56.0 but it still produced the same error as above.

- [https://stackoverflow.com/questions/76264783/bertopic-guided-topic-modelling-returns-a-valueerror-inhomogenous-shape](url) I have also tried downgrading numpy to version 1.23.5 but the same error was also produced.

- I tried to replicate the example provided in the documentation with the above solutions I attempted but it still produced the same error.

I can't find any other resources online about this so thought I would open a new one.

Here are a list of my current packages
```
Package Version
------------------------- -----------
aiohttp 3.9.3
aiosignal 1.3.1
annotated-types 0.6.0
anyio 4.3.0
appdirs 1.4.4
asn1crypto 1.5.1
asttokens 2.4.1
async-timeout 4.0.3
attrs 23.2.0
Automat 22.10.0
bcrypt 4.1.2
beautifulsoup4 4.12.3
bertopic 0.16.1
blis 0.7.11
botocore 1.34.54
catalogue 2.0.10
certifi 2024.2.2
cffi 1.16.0
charset-normalizer 3.3.2
click 8.1.7
cloudpathlib 0.16.0
comm 0.2.2
confection 0.1.4
constantly 23.10.4
contourpy 1.2.1
cryptography 42.0.5
cssselect 1.2.0
cycler 0.12.1
cymem 2.0.8
Cython 0.29.37
debugpy 1.8.1
decorator 5.1.1
deep-translator 1.11.4
distro 1.9.0
docutils 0.20.1
en-core-web-sm 3.7.1
et-xmlfile 1.1.0
exceptiongroup 1.2.0
executing 2.0.1
fastjsonschema 2.19.1
filelock 3.13.1
fonttools 4.51.0
frozenlist 1.4.1
fsspec 2024.3.1
gensim 4.3.2
h11 0.14.0
hdbscan 0.8.33
httpcore 1.0.4
httpx 0.27.0
huggingface-hub 0.23.0
hyperlink 21.0.0
idna 3.6
incremental 22.10.0
ipykernel 6.29.4
ipympl 0.9.4
ipython 8.23.0
ipython-genutils 0.2.0
ipywidgets 8.1.2
itemadapter 0.8.0
itemloaders 1.1.0
jedi 0.19.1
Jinja2 3.1.3
jmespath 1.0.1
joblib 1.4.2
jsonschema 4.22.0
jsonschema-specifications 2023.12.1
jupyter_client 8.6.1
jupyter_core 5.7.2
jupyterlab_widgets 3.0.10
kiwisolver 1.4.5
langcodes 3.4.0
langdetect 1.0.9
language_data 1.2.0
llvmlite 0.39.1
lxml 5.1.0
marisa-trie 1.1.1
MarkupSafe 2.1.5
matplotlib 3.8.4
matplotlib-inline 0.1.7
mpmath 1.3.0
multidict 6.0.5
murmurhash 1.0.10
nbformat 5.10.4
nest-asyncio 1.6.0
networkx 3.3
numba 0.56.0
numpy 1.22.4
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.20.5
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.1.105
openai 1.14.1
openpyxl 3.1.2
packaging 23.2
pandas 2.2.1
paramiko 3.4.0
parsel 1.8.1
parso 0.8.4
pexpect 4.9.0
pillow 10.3.0
pip 24.0
platformdirs 4.2.1
plotly 5.22.0
preshed 3.0.9
prompt-toolkit 3.0.43
Protego 0.3.0
psutil 5.9.8
ptyprocess 0.7.0
pure-eval 0.2.2
pyasn1 0.5.1
pyasn1-modules 0.3.0
pycparser 2.21
pydantic 2.6.4
pydantic_core 2.16.3
PyDispatcher 2.0.7
Pygments 2.17.2
PyNaCl 1.5.0
pynndescent 0.5.12
pyOpenSSL 24.0.0
pyparsing 3.1.1
PySocks 1.7.1
python-dateutil 2.9.0.post0
python-docx 1.1.0
python-dotenv 1.0.1
pytz 2024.1
PyYAML 6.0.1
pyzmq 26.0.2
queuelib 1.6.2
referencing 0.35.1
regex 2024.4.28
requests 2.31.0
requests-file 2.0.0
rpds-py 0.18.0
safetensors 0.4.3
scikit-learn 1.4.2
scipy 1.10.1
Scrapy 2.11.1
seaborn 0.13.2
sentence-transformers 2.7.0
service-identity 24.1.0
setuptools 69.5.1
six 1.16.0
smart-getenv 1.1.0
smart-open 6.4.0
sniffio 1.3.1
soupsieve 2.5
spacy 3.7.4
spacy-legacy 3.0.12
spacy-loggers 1.0.5
srsly 2.4.8
stack-data 0.6.3
sympy 1.12
tenacity 8.2.3
thinc 8.2.3
threadpoolctl 3.5.0
tldextract 5.1.1
tokenizers 0.19.1
torch 2.3.0
tornado 6.4
tqdm 4.66.2
traitlets 5.14.3
transformers 4.40.1
triton 2.3.0
Twisted 24.3.0
typer 0.9.4
typing_extensions 4.10.0
tzdata 2024.1
umap-learn 0.5.6
urllib3 2.0.7
vaderSentiment 3.3.2
virustotal-python 1.0.2
vt-graph-api 2.2.0
vt-py 0.18.0
w3lib 2.1.2
wasabi 1.1.2
wcwidth 0.2.13
weasel 0.3.4
wheel 0.43.0
widgetsnbextension 4.0.10
wordcloud 1.9.3
wrapt 1.16.0
yarl 1.9.4
zope.interface 6.2
```

Start at the BERTopic constructor using seed_topic_list and fit_transform with the provided example and package versions, then compare it with the guided topic modeling documentation example. Reproduce the ValueError and trace which input or dependency causes the inhomogeneous array; done means guided modeling runs without that error.

Written by the indexing model from the issue text.