Ë ØWŠiñãóN—ddlZddlmZddlmZddlmZmZm Z Gd„de«Z y)éN)Ú csr_matrix)Ú BaseEstimator)ÚMappingÚListÚTuplec óp—eZdZdZdej dedeee e ee ffdeee e ee fffd„Z y)ÚBaseRepresentationzDThe base representation model for fine-tuning topic representations.Ú documentsÚc_tf_idfÚtopicsÚreturncó—|jS)a¥Extract topics. Each representation model that inherits this class will have its arguments (topic_model, documents, c_tf_idf, topics) automatically passed. Therefore, the representation model will only have access to the information about topics related to those arguments. Arguments: topic_model: The BERTopic model that is fitted until topic representations are calculated. documents: A dataframe with columns "Document" and "Topic" that contains all documents with each corresponding topic. c_tf_idf: A c-TF-IDF representation that is typically identical to `topic_model.c_tf_idf_` except for dynamic, class-based, and hierarchical topic modeling where it is calculated on a subset of the documents. topics: A dictionary with topic (key) and tuple of word and weight (value) as calculated by c-TF-IDF. This is the default topics that are returned if no representation model is used. )Útopic_representations_)ÚselfÚ topic_modelr r r s úi/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/bertopic/representation/_base.pyÚextract_topicsz!BaseRepresentation.extract_topics s€ð<×1Ñ1Ð1óN) Ú__name__Ú __module__Ú __qualname__Ú__doc__ÚpdÚ DataFramerrÚstrrrÚfloatr©rrr r sf„ÙNð2ð—<‘<ð2ðð 2𠘘T %¨¨U¨ Ñ"3Ñ4Ð4Ñ5ð 2ð d˜5  e Ñ,Ñ-Ð-Ñ .ô 2rr ) ÚpandasrÚ scipy.sparserÚ sklearn.baserÚtypingrrrr rrrúr"s ðÛÝ#Ý&ß'Ñ'ô!2˜õ!2r