Class Model2VecBackend

bertopic/backend/_model2vec.py:9–129

Model2Vec embedding model. Arguments: embedding_model: Either a model2vec model or a string pointing to a model2vec model distill: Indicates whether to distill a sentence-transformers compatible model. The distillation will happen during

class Model2VecBackend(BaseEmbedder):
    """Model2Vec embedding model.

    Arguments:
        embedding_model: Either a model2vec model or a
                         string pointing to a model2vec model
        distill: Indicates whether to distill a sentence-transformers compatible model.
                 The distillation will happen during fitting of the topic model.
                 NOTE: Only works if `embedding_model` is a string.
        distill_kwargs: Keyword arguments to pass to the distillation process
                        of `model2vec.distill.distill`
        distill_vectorizer: A CountVectorizer used for creating a custom vocabulary
                            based on the same documents used for topic modeling.
                            NOTE: If "vocabulary" is in `distill_kwargs`, this will be ignored.

    Examples:
    To create a model, you can load in a string pointing to a
    model2vec model:

    ```python
    from bertopic.backend import Model2VecBackend

    sentence_model = Model2VecBackend("minishlab/potion-base-8M")
    ```

    or  you can instantiate a model yourself:

    ```python
    from bertopic.backend import Model2VecBackend
    from model2vec import StaticModel

    embedding_model = StaticModel.from_pretrained("minishlab/potion-base-8M")
    sentence_model = Model2VecBackend(embedding_model)
    ```

    If you want to distill a sentence-transformers model with the vocabulary of the documents,
    run the following:

    ```python
    from bertopic.backend import Model2VecBackend

    sentence_model = Model2VecBackend("sentence-transformers/all-MiniLM-L6-v2", distill=True)
    ```
    """

    def __init__(
        self,
        embedding_model: Union[str, StaticModel],
        distill: bool = False,
        distill_kwargs: dict = {},
        distill_vectorizer: str | None = None,
    ):
        super().__init__()

        self.distill = distill
        self.distill_kwargs = distill_kwargs
        self.distill_vectorizer = distill_vectorizer
        self._has_distilled = False