BERTopic
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How to get the respective topics, the name of each topic, the top n words of each topic and other data for news docs on which `transform()` is used?
Suppose I trained the model first and got the topics, representative docs etc of the training docs using .get_document_info()
:
topic_model = BERTopic(vectorizer_model=vectorizer_model, hdbscan_model=hdbscan_model, embedding_model=embedding_model)
topics, probs = topic_model.fit_transform(docs)
print(topic_model.get_document_info())
and now I am predicting topics over new_docs
:
new_topics, new_probs = topic_model.transform(new_docs)
now how do I get the information which new_doc
falls in which new_topic
? Like how can I generate a list/df just same as .get_document_info()
for the newer docs and its new topics?