Running Keybert for a list of docs to extract arabic keywords

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Hi I have a list of arabic text and I want to extract keywords of each list element, for this I'm following the documentation ,
So I started by initiating the keybert model with this model
`from keybert import KeyBERT
kw_model = KeyBERT(model="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") `
then to extract doc and word embedding I did this
`doc_embeddings, word_embeddings = kw_model.extract_embeddings(doc, min_df=1, stop_words= stp.stopwords_list())
` with stp.stopwords_list() is my custom stop words for arabic
But this last line throws an error:
`ValueError: not enough values to unpack (expected 2, got 0)`
edit: doc is a list of text , len of doc is 108789, each text is an article
I didn't understand why this error and how to figure it out.
Thank you

Start with the documented KeyBERT usage and the kw_model.extract_embeddings call shown in the issue, reproducing it with the Arabic stop-word list and a small sample before considering the full 108,789-item list. Trace where extract_embeddings produces no values and compare the call with the model's documented input expectations. Done means the cause of the unpacking error is established and the issue includes a verified correction or limitation.

Written by the indexing model from the issue text.