Highlights Cleaned up documentation and added several visual representations of the algorithm (excluding MMR / MaxSum) Added functions to extract and pass word- and document embeddings which should make fine-tuning much faster from keybert import KeyBERT kw_model = KeyBERT() # Prepare embeddings doc_embeddings, word_embeddings = kw_model.extract_embeddings(docs) # Extract keywords without needing to re-calculate embeddings keywords = kw_model.extract_keywords(docs, doc_embeddings=doc_embeddings, word_embeddings=word_embeddings) Do note that the parameters passed to .extract_embeddings for creating the vectorizer should be exactly the same as those in .extract_keywords. Fixes Redundant documentation was removed by @mabhay3420 in #123 Fixed Gensim backend not working after v4 migration (#71) Fixed candidates not working (#122)