How can one use Sentence Transformers for clustering sentences or documents by topic or content similarity?

To use Sentence Transformers for clustering sentences or documents by topic or similarity, you first convert text into numerical embeddings (dense vectors) that capture semantic meaning. These embeddings allow algorithms to compare content by measuring vector similarity, such as cosine similarity. Sentence Transformers models, like all-MiniLM-L6-v2 or paraphrase-mpnet-base-v2, are trained to produce embeddings where similar texts map closer in vector space. For example, the sentence “Machine learning is powerful” and “AI algorithms can analyze data” might have closer embeddings than either would to “How to bake a cake,” reflecting their topical alignment.

Start by installing the sentence-transformers library and generating embeddings for all texts. For a dataset of 1,000 documents, you’d pass each through the model to get a 384- or 768-dimensional vector (depending on the model). This step is computationally efficient on GPUs but manageable on CPUs for smaller datasets. Once embeddings are generated, use clustering algorithms like K-means, DBSCAN, or hierarchical clustering. For example, K-means groups embeddings into k clusters by minimizing intra-cluster distance. You’d set k based on domain knowledge or metrics like the elbow method. Alternatively, density-based methods like HDBSCAN automatically determine cluster counts, which is useful when the number of topics is unknown.

After clustering, validate results using metrics like silhouette score (measuring cluster cohesion) or manual inspection. For instance, if two clusters contain sentences like “Cloud computing reduces costs” and “AWS offers scalable servers,” they likely represent the same topic and should be merged. To improve performance, consider dimensionality reduction (e.g., UMAP) before clustering if embeddings are high-dimensional. For large datasets, approximate nearest neighbor libraries like FAISS or Annoy can speed up similarity searches. Practical tools like scikit-learn’s KMeans or AgglomerativeClustering integrate easily with embeddings, enabling workflows such as grouping customer feedback into themes or organizing research papers by subject.