# 获取所有文档的主题分配信息 doc_info = topic_model.get_document_info(content) print(doc_info) # 筛选出主题编号为 16 的文档 topic_number = 16 topic_docs = doc_info[doc_info["Topic"] == topic_number] # 获取该主题对应的文本 texts_in_topic = topic_docs["Document"].tolist() # 打印...
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) Document Topic Name Top_n_words Probability ... I am sure ...
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。