Get all document information .get_document_info(docs) Get representative docs per topic .get_representative_docs() Update topic representation .update_topics(docs, n_gram_range=(1, 3)) Generate topic labels .generate_topic_labels() Set topic labels .set_topic_labels(my_custom_labels) Merge to...
Using.get_document_info, we can also extract information on a document level, such as their corresponding topics, probabilities, whether they are representative documents for a topic, etc.: >>>topic_model.get_document_info(docs)DocumentTopicNameTop_n_wordsProbability...IamsuresomebashersofPens.....
老实说，我不确定这里发生了什么。我相信还有一个同样的问题没有解决，但它可能与底层的T5模型有关。
Using.get_document_info, we can also extract information on a document level, such as their corresponding topics, probabilities, whether they are representative documents for a topic, etc.: >>>topic_model.get_document_info(docs)DocumentTopicNameTop_n_wordsProbability...IamsuresomebashersofPens.....