Class BERTopic

bertopic/_bertopic.py:88–4884

BERTopic is a topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. The default embedding model is `all-MiniLM-L6-v2` when selecting `language="engl

class BERTopic:
    """BERTopic is a topic modeling technique that leverages BERT embeddings and
    c-TF-IDF to create dense clusters allowing for easily interpretable topics
    whilst keeping important words in the topic descriptions.

    The default embedding model is `all-MiniLM-L6-v2` when selecting `language="english"`
    and `paraphrase-multilingual-MiniLM-L12-v2` when selecting `language="multilingual"`.

    Attributes:
        topics_ (List[int]) : The topics that are generated for each document after training or updating
                              the topic model. The most recent topics are tracked.
        probabilities_ (List[float]): The probability of the assigned topic per document. These are
                                      only calculated if a HDBSCAN model is used for the clustering step.
                                      When `calculate_probabilities=True`, then it is the probabilities
                                      of all topics per document.
        topic_sizes_ (Mapping[int, int]) : The size of each topic.
        topic_mapper_ (TopicMapper) : A class for tracking topics and their mappings anytime they are
                                      merged, reduced, added, or removed.
        topic_representations_ (Mapping[int, Tuple[int, float]]) : The top n terms per topic and their respective
                                                                   c-TF-IDF values.
        c_tf_idf_ (csr_matrix) : The topic-term matrix as calculated through c-TF-IDF. To access its respective
                                 words, run `.vectorizer_model.get_feature_names()`  or
                                 `.vectorizer_model.get_feature_names_out()`
        topic_labels_ (Mapping[int, str]) : The default labels for each topic.
        custom_labels_ (List[str]) : Custom labels for each topic.
        topic_embeddings_ (np.ndarray) : The embeddings for each topic. They are calculated by taking the
                                         centroid embedding of each cluster.
        representative_docs_ (Mapping[int, str]) : The representative documents for each topic.

    Examples:
    ```python
    from bertopic import BERTopic
    from sklearn.datasets import fetch_20newsgroups

    docs = fetch_20newsgroups(subset='all')['data']
    topic_model = BERTopic()
    topics, probabilities = topic_model.fit_transform(docs)
    ```

    If you want to use your own embedding model, use it as follows:

    ```python
    from bertopic import BERTopic
    from sklearn.datasets import fetch_20newsgroups
    from sentence_transformers import SentenceTransformer

    docs = fetch_20newsgroups(subset='all')['data']
    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
    topic_model = BERTopic(embedding_model=sentence_model)
    ```

    Due to the stochastic nature of UMAP, the results from BERTopic might differ
    and the quality can degrade. Using your own embeddings allows you to
    try out BERTopic several times until you find the topics that suit
    you best.
    """

    def __init__(