Method update_topics

bertopic/_bertopic.py:1488–1596

Updates the topic representation by recalculating c-TF-IDF with the new parameters as defined in this function. When you have trained a model and viewed the topics and the words that represent them, you might not be satisfied with the representation. Perhaps you forgot to re

(
        self,
        docs: List[str],
        images: List[str] | None = None,
        topics: List[int] | None = None,
        top_n_words: int = 10,
        n_gram_range: Tuple[int, int] | None = None,
        vectorizer_model: CountVectorizer = None,
        ctfidf_model: ClassTfidfTransformer = None,
        representation_model: BaseRepresentation = None,
    )

        return similar_topics, similarity

    def update_topics(
        self,
        docs: List[str],
        images: List[str] | None = None,
        topics: List[int] | None = None,
        top_n_words: int = 10,
        n_gram_range: Tuple[int, int] | None = None,
        vectorizer_model: CountVectorizer = None,
        ctfidf_model: ClassTfidfTransformer = None,
        representation_model: BaseRepresentation = None,
    ):
        """Updates the topic representation by recalculating c-TF-IDF with the new
        parameters as defined in this function.

        When you have trained a model and viewed the topics and the words that represent them,
        you might not be satisfied with the representation. Perhaps you forgot to remove
        stop_words or you want to try out a different n_gram_range. This function allows you
        to update the topic representation after they have been formed.

        Arguments:
            docs: The documents you used when calling either `fit` or `fit_transform`
            images: The images you used when calling either `fit` or `fit_transform`
            topics: A list of topics where each topic is related to a document in `docs`.
                    Use this variable to change or map the topics.
                    NOTE: Using a custom list of topic assignments may lead to errors if
                          topic reduction techniques are used afterwards. Make sure that
                          manually assigning topics is the last step in the pipeline
            top_n_words: The number of words per topic to extract. Setting this
                         too high can negatively impact topic embeddings as topics
                         are typically best represented by at most 10 words.
            n_gram_range: The n-gram range for the CountVectorizer.
            vectorizer_model: Pass in your own CountVectorizer from scikit-learn
            ctfidf_model: Pass in your own c-TF-IDF model to update the representations
            representation_model: Pass in a model that fine-tunes the topic representations
                                  calculated through c-TF-IDF. Models from `bertopic.representation`
                                  are supported.

        Examples:
        In order to update the topic representation, you will need to first fit the topic
        model and extract topics from them. Based on these, you can update the representation:

        ```python
        topic_model.update_topics(docs, n_gram_range=(2, 3))
        ```

        You can also use a custom vectorizer to update the representation:

        ```python
        from sklearn.feature_extraction.text import CountVectorizer
        vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words="english")
        topic_model.update_topics(docs, vectorizer_model=vectorizer_model)
        ```

        You can also use this function to change or map the topics to something else.
        You can update them as follows:

        ```python
        topic_model.update_topics(docs, my_updated_topics)