To use Sentence Transformers for clustering sentences or documents by topic or similarity, you first convert text into numerical embeddings that capture semantic meaning, then apply clustering algorithms to group similar embeddings. Here’s a step-by-step breakdown:
1. Generate Embeddings
Sentence Transformers (e.g., models like all-MiniLM-L6-v2 or multi-qa-mpnet-base) convert text into dense vector representations. These embeddings map semantically similar sentences closer in vector space. For example, the sentences "climate change impacts ecosystems" and "global warming affects biodiversity" would have embeddings with high cosine similarity. You load a pre-trained model, process your text data (cleaning if needed), and generate embeddings for all sentences/documents. This step is efficient and scalable, even for large datasets, thanks to optimized transformer architectures.
2. Apply Clustering Algorithms Once embeddings are generated, use algorithms like K-means, DBSCAN, or HDBSCAN to group similar vectors. K-means is simple but requires specifying the number of clusters (k), which can be estimated using the elbow method or silhouette score. DBSCAN is better for irregularly shaped clusters and automatically detects noise (outliers). For example, customer reviews like "delivery was slow" and "shipping took too long" might cluster together under a "delivery issues" topic. Dimensionality reduction (e.g., PCA or UMAP) can improve clustering performance by reducing noise in high-dimensional embeddings.
3. Evaluate and Refine
Validate clusters using metrics like silhouette score (measures separation between clusters) or manually inspect samples. For instance, if two clusters both contain tech-related articles, you might adjust the model (e.g., try a larger transformer) or fine-tune the Sentence Transformer on domain-specific data for better embeddings. Iterate by tweaking hyperparameters (e.g., DBSCAN’s eps for cluster density) or switching algorithms until clusters align with semantic topics. Visualization tools like t-SNE or Plotly can help inspect cluster coherence.
By combining semantic embeddings with clustering, you can organize unstructured text into meaningful groups without labeled data, enabling tasks like topic modeling, document categorization, or anomaly detection.