要选择一个不同的预训练嵌入模型，我们只需将其通过BERTopic，将变量embedding_model指向相应的sentence-transformers模型：from bertopic import BERTopicmodel = BERTopic(embedding_model="xlm-r-bert-base-nli-stsb-mean-tokens")单击此处查看受支持的sentence-transformers模型列表:https://www.sbert.net/docs/pret...
bertopic是基于BERT模型的话题建模工具。它的参数包括: 1. num_topics: 指定要生成的主题数目。默认值为10。 2. embedding_model: 使用的嵌入模型,可以是"bert-base-uncased"、"bert-large-uncased"或其他可用的BERT模型。默认值为"bert-base-uncased"。 3. nr_topics_from_umap: 从UMAP生成的嵌入中选择的主题...
要选择一个不同的预训练嵌入模型,我们只需将其通过BERTopic,将变量embedding_model指向相应的sentence-transformers模型: from bertopic import BERTopic model = BERTopic(embedding_model="xlm-r-bert-base-nli-stsb-mean-tokens") 单击此处查看受支持的sentence-transformers模型列表:https://www.sbert.net/docs/p...
hdbscan_model = HDBSCAN(min_cluster_size=400, metric='euclidean', cluster_selection_method='eom', prediction_data=True) 第六步:训练模型 from bertopic import BERTopic topic_model = BERTopic( # Sub-models embedding_model=embedding_model, umap_model=umap_model, hdbscan_model=hdbscan_model, repre...
32 通俗易懂的BERTopic教程—主题聚类:使用不同的词嵌入Embedding 910 3 20:46 App 28 通俗易懂的BERTopic教程—主题聚类:减少离群值总结,reduce_outliers(),重要⭐ 930 -- 10:10 App 27 通俗易懂的BERTopic教程—主题聚类:文档-主题概率 650 -- 5:04 App 主题模型可视化+ai分析(包括lda、dtm以及ber...
frombertopic.representationimportKeyBERTInspired# Create your representation model# KeyBERTInspired可以减少stop wordsrepresentation_model = KeyBERTInspired() 然后把模型组装起来,训练模型 topic_model = BERTopic( embedding_model=embedding_model,# Step 1 - Extract embeddingsumap_model=umap_model,# Step 2 ...
一:主题模型有很多种,现在比较常用的是PLSA和LDA这两种主题模型,还有Unigram model和mixture Unigrams model我将循序渐进的说一下这四种模型: 1:Unigram model思想:这种方法只是根据先验概率去生成文档,首先我们要有一篇已知文档W=(w1,w2,...wn),p(wn)表示单词wn的先验概率,所以生成的文档p(w)=p(w1)****p ...
frombertopicimportBERTopic importspacy zh_model=spacy.load("zh_core_web_sm") topic_model=BERTopic(language="chinese(simplified)", embedding_model=zh_model, calculate_probabilities=True, verbose=True) docs=df['content'].tolist() #2000条进行fit_transform需要1min topics,probs=topic_model.fit_tr...
model.visualize_distribution(probabilities[0]) 04 Embedding Models You can select any model fromsentence-transformersand pass it through BERTopic withembedding_model: frombertopicimportBERTopicmodel = BERTopic(embedding_model="xlm-r-bert-base-nli-stsb-mean-tokens") ...
Dissimilar topics are added to the baseline model whereas similar topics are assigned to the topic of the baseline. This means that we need the embedding models to be the same. When merging BERTopic models, duplicate topics will be merged and all other topics will be kept the same. ...