Wi 6ddlmZddlmZmZGddeZy)) BaseEmbedder)check_is_fittedNotFittedErrorc*eZdZdZfdZddZxZS)SklearnEmbedderaScikit-Learn based embedding model. This component allows the usage of scikit-learn pipelines for generating document and word embeddings. Arguments: pipe: A scikit-learn pipeline that can `.transform()` text. Examples: Scikit-Learn is very flexible and it allows for many representations. A relatively simple pipeline is shown below. ```python from sklearn.pipeline import make_pipeline from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import TfidfVectorizer from bertopic.backend import SklearnEmbedder pipe = make_pipeline( TfidfVectorizer(), TruncatedSVD(100) ) sklearn_embedder = SklearnEmbedder(pipe) topic_model = BERTopic(embedding_model=sklearn_embedder) ``` This pipeline first constructs a sparse representation based on TF/idf and then makes it dense by applying SVD. Alternatively, you might also construct something more elaborate. As long as you construct a scikit-learn compatible pipeline, you should be able to pass it to Bertopic. !!! Warning One caveat to be aware of is that scikit-learns base `Pipeline` class does not support the `.partial_fit()`-API. If you have a pipeline that theoretically should be able to support online learning then you might want to explore the [scikit-partial](https://github.com/koaning/scikit-partial) project. c0t|||_y)N)super__init__pipe)selfr __class__s e/home/sietch6/trending-topics-pipeline/venv/lib/python3.12/site-packages/bertopic/backend/_sklearn.pyr zSklearnEmbedder.__init__.s  c t|j|jj|}|S#t$r|jj |}Y|SwxYw)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: No-op variable that's kept around to keep the API consistent. If you want to get feedback on training times, you should use the sklearn API. Returns: Document/words embeddings with shape (n, m) with `n` documents/words that each have an embeddings size of `m` )rr transformr fit_transform)r documentsverbose embeddingss rembedzSklearnEmbedder.embed2sZ < DII &,,Y7J <00;J rs)D?l?r