[inhomogeneous shape unresolved] [Colab] 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.

Nobody has claimed this yet.

Using BERTopic for some research task, I am getting:
`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`

Is there plan for `BERTopic` supporting `numpy >1.23.5` and `numba > 0.56.4`?
This will make it easier to use BERTopic in Colab without having to downgrade persistently manually (`!pip install numba==0.56.4`): especially for those without Colab.pro
Thanks.

[**Noted**]
Related issue: [**closed**][1] | [#1697, #1602, #1309]
Related issue: [**open**][2] | [#1814, #1799, #1684, #1584, #1421, #1269]
**SO**: https://stackoverflow.com/a/76504825

PS: I gather that a walkaround is to downgrade numpy!
in issue #1421: @aaron-imani hinted at `np.average()` in `_guided_topic_modelling()`. @MaartenGR indicated that setting `numba` to `0.56.4` or earlier should ideally fix the issue

Thanks @MaartenGr for your insightful engagement in previous issues and suggestions.

**Environment**: `colab.research.google.com`
NB: (as at 26 April 2024, Colab runs `python: 3.10.12, bertopic: 0.16.1, numpy: 1.25.2, numba: 0.58.1`)

**BERTopic**:
```python
# Install BERTopic
!pip install bertopic
# Load libraries
from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired
from bertopic.vectorizers import ClassTfidfTransformer
from umap import UMAP
from sentence_transformers import SentenceTransformer
```
[Code snippet]
```python
# Train BERTopic

topic_model_03 = BERTopic(
verbose=True,
min_topic_size= 5, #10, #12, #15, ## the higher, the lower the clusters/topics
nr_topics = 4, #5, ## reduce the initial number of topics
#seed_topic_list=seed_topic_list, ##ValueError: ...
n_gram_range = (1,3), ## n-gram range for the CountVectorizer
zeroshot_topic_list=zeroshot_topic_list,
zeroshot_min_similarity=.35, #.55, #.75, #.85,
embedding_model="thenlper/gte-small", ## pass string directly to sbert sentence-transformers models
#umap_model = umap_model, ## dimensionality
ctfidf_model = ctfidf_model,
representation_model=KeyBERTInspired(),
)
```
The seed topics look like this {redacted in part}
```python
### Try with Guided Representation
seed_topic_list = [["sustainability", "...", "sustain", "...", "..."],
["climate change", "climate", "...", "...", "...", "...", "ozone"],
["social justice", "social", "...", "...", "...", "...", "...", "..."],
["net zero", "..."]
]

## fit and transform
#topics_03, probs_03 = topic_model_03.fit_transform(docs)
## visualise documents' topics spread
#topic_model_03.visualize_documents(docs)
```

[1]: https://github.com/MaartenGr/BERTopic/issues?q=is%3Aissue+inhomogeneous+is%3Aclosed
[2]: https://github.com/MaartenGr/BERTopic/issues?q=is%3Aissue+inhomogeneous+is%3Aopen

Start by reproducing the BERTopic.fit_transform example in Colab with the reported Python, BERTopic, NumPy, and numba versions. Read related issues #1421, #1584, #1684, #1799, and #1814; done means identifying a supported dependency combination or a confirmed BERTopic change that removes the need to downgrade NumPy or numba.

Written by the indexing model from the issue text.