I've used BERTopic with success for the following tasks: get topics, visualise (topics, barcharts, documents ...) and DTM (extended to get area plot with considerable success).  

However, I am unable to use the find_topics() function
> *(There are a few others I'm struggling with, which I'll post as new questions so as not to conflate this one).*  

I get an error message indicating that I'm using embedding (which is true).

```python
# Prepare embeddings using default 'sentence embedding'
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs_bert, show_progress_bar=True)
```

Trying to solve that, I have tried to instantiate a new model without embedding
```python
model_ngram_embed2 = BERTopic(embedding_model=embeddings)
```
but it then throws an error:
> ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I need to instantiate before I can fit_transform the model to my doc (text corpus), after which I would then be able to find_topics().  
How do I go about that? What should be done?

Regarding find_topics(), I've read allowing *[precomputed embeddings in bertopic.find_topics()][1]* issue   
NB: *Python 3.8.8 | IPython 7.31.1 | BERTopic 0.11.0*


  [1]: https://github.com/MaartenGr/BERTopic/issues/79