BaseRepresentation

The base representation model for fine-tuning topic representations.

Source code in bertopic\representation\_base.py

class BaseRepresentation(BaseEstimator):
    """The base representation model for fine-tuning topic representations."""

    def extract_topics(
        self,
        topic_model,
        documents: pd.DataFrame,
        c_tf_idf: csr_matrix,
        topics: Mapping[str, List[Tuple[str, float]]],
    ) -> Mapping[str, List[Tuple[str, float]]]:
        """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.
        """
        return topic_model.topic_representations_

extract_topics(self, topic_model, documents, c_tf_idf, topics)

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.

Parameters:

Name	Type	Description	Default
topic_model		The BERTopic model that is fitted until topic representations are calculated.	required
documents	DataFrame	A dataframe with columns "Document" and "Topic" that contains all documents with each corresponding topic.	required
c_tf_idf	csr_matrix	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.	required
topics	Mapping[str, List[Tuple[str, float]]]	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.	required

Source code in bertopic\representation\_base.py

def extract_topics(
    self,
    topic_model,
    documents: pd.DataFrame,
    c_tf_idf: csr_matrix,
    topics: Mapping[str, List[Tuple[str, float]]],
) -> Mapping[str, List[Tuple[str, float]]]:
    """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.
    """
    return topic_model.topic_representations_