I've used BERTopic with success for the following tasks: get topics, visualise and DTM.

However, I am unable to use the find_topics() function. I get an error message indicating that I'm using embedding (which is true).

# 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

model_ngram_embed2 = BERTopic(embedding_model=embeddings)

but it then throws another 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().

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

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).

# 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

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() issue
NB: Python 3.8.8 | IPython 7.31.1 | BERTopic 0.11.0

#Python #BERTopic #NLP
I've used BERTopic with success: get topics, visualise and DTM.
However, I am unable to use the find_topics() function. I get an error message indicating that I'm using embedding. This is true.

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

However, trying to instantiate a new model without embedding

model_ngram_embed2 = BERTopic(embedding_model=embeddings)

throws an error

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

PS: 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().

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

I've used BERTopic with success for the following tasks: get topics, visualise and DTM.

However, I am unable to use the find_topics() function. I get an error message indicating that I'm using embedding (which is true).

# 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

model_ngram_embed2 = BERTopic(embedding_model=embeddings)

but it then throws another 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().

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