Wi,ddlZddlmZGddZy)N)ListceZdZdZd dZd deededejfdZ d deededejfd Z d d eededejfd Z y) BaseEmbedderaThe Base Embedder used for creating embedding models. Arguments: embedding_model: The main embedding model to be used for extracting document and word embedding word_embedding_model: The embedding model used for extracting word embeddings only. If this model is selected, then the `embedding_model` is purely used for creating document embeddings. Nc ||_||_y)N)embedding_modelword_embedding_model)selfrrs b/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/bertopic/backend/_base.py__init__zBaseEmbedder.__init__s.$8! documentsverbosereturncy)aEmbed a list of n documents/words into an n-dimensional matrix of embeddings. Arguments: documents: A list of documents or words to be embedded verbose: Controls the verbosity of the process Returns: Document/words embeddings with shape (n, m) with `n` documents/words that each have an embeddings size of `m` N)r r rs r embedzBaseEmbedder.embeds r wordsc&|j||S)aZEmbed a list of n words into an n-dimensional matrix of embeddings. Arguments: words: A list of words to be embedded verbose: Controls the verbosity of the process Returns: Word embeddings with shape (n, m) with `n` words that each have an embeddings size of `m` r)r rrs r embed_wordszBaseEmbedder.embed_words#szz%))r documentc&|j||S)ahEmbed a list of n words into an n-dimensional matrix of embeddings. Arguments: document: A list of documents to be embedded verbose: Controls the verbosity of the process Returns: Document embeddings with shape (n, m) with `n` documents that each have an embeddings size of `m` r)r rrs r embed_documentszBaseEmbedder.embed_documents2szz(G,,r )NN)F) __name__ __module__ __qualname____doc__r rstrboolnpndarrayrrrrr r rrsr 9  tCy  4  BJJ   *c *T *bjj * -S -D -RZZ -r r)numpyr typingrrrr r r$s9-9-r