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Update _bertopic.py to address #2316 #2317

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Update _bertopic.py to address #2316 #2317

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9 changes: 8 additions & 1 deletion bertopic/_bertopic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1488,6 +1488,7 @@ def update_topics(
vectorizer_model: CountVectorizer = None,
ctfidf_model: ClassTfidfTransformer = None,
representation_model: BaseRepresentation = None,
embeddings: np.ndarray = None,
):
"""Updates the topic representation by recalculating c-TF-IDF with the new
parameters as defined in this function.
Expand All @@ -1514,6 +1515,7 @@ def update_topics(
representation_model: Pass in a model that fine-tunes the topic representations
calculated through c-TF-IDF. Models from `bertopic.representation`
are supported.
embeddings: Pre-trained document embeddings.

Examples:
In order to update the topic representation, you will need to first fit the topic
Expand Down Expand Up @@ -1586,7 +1588,12 @@ def update_topics(
if same_position and -1 not in topics and -1 in self.topics_:
self.topic_embeddings_ = self.topic_embeddings_[1:]
else:
self._create_topic_vectors()
if embeddings is not None:
# Use provided embeddings
self._create_topic_vectors(documents=documents, embeddings=embeddings)
else:
# Use self.embedding_model to calculate embeddings
self._create_topic_vectors()

def get_topics(self, full: bool = False) -> Mapping[str, Tuple[str, float]]:
"""Return topics with top n words and their c-TF-IDF score.
Expand Down
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