from sentence_transformers import SentenceTransformer # Step 1 - Extract embeddings embedding_model = SentenceTransformer("C:\\Users\\18267\\.cache\\huggingface\\hub\\models--sentence-transformers--all-mpnet-base-v2\\snapshots\\9a3225965996d404b775526de6dbfe85d3368642") embeddings = np.load('clean_emb_last.npy') print(f"嵌入的形状: {embeddings.shape}") # Step 2 - Reduce dimensionality umap_model = UMAP(n_neighbors=7, n_components=10, min_dist=0.0, metric='cosine',random_state=42) # Step 3 - Cluster reduced embeddings hdbscan_model = HDBSCAN(min_samples=7, min_cluster_size=60,metric='euclidean', cluster_selection_method='eom', prediction_data=True) # Step 4 - Tokenize topics # Combine custom stop words with scikit-learn's English stop words custom_stop_words = ['h2', 'storing', 'storage', 'include', 'comprise', 'utility', 'model', 'disclosed', 'embodiment', 'invention', 'prior', 'art', 'according', 'present', 'method', 'system', 'device', 'may', 'also', 'use', 'used', 'provide', 'wherein', 'configured', 'predetermined', 'plurality', 'comprising', 'consists', 'following', 'characterized', 'claim', 'claims', 'said', 'first', 'second', 'third', 'fourth', 'fifth', 'one', 'two', 'three','hydrogen'] # Create combined stop words set all_stop_words = set(custom_stop_words).union(ENGLISH_STOP_WORDS) vectorizer_model = CountVectorizer(stop_words=list(all_stop_words)) # Step 5 - Create topic representation ctfidf_model = ClassTfidfTransformer() # All steps together topic_model = BERTopic( embedding_model=embedding_model, # Step 1 - Extract embeddings umap_model=umap_model, # Step 2 - Reduce dimensionality hdbscan_model=hdbscan_model, # Step 3 - Cluster reduced embeddings vectorizer_model=vectorizer_model, # Step 4 - Tokenize topics ctfidf_model=ctfidf_model, # Step 5 - Extract topic words calculate_probabilities=True )代码如上为什么拟合后每个主题只包含10个主题词