To perform paraphrase mining with Sentence Transformers, you start by converting sentences into dense vector representations (embeddings) using a pre-trained model, then compare these embeddings to identify semantically similar pairs. Here's a step-by-step breakdown:
Model Selection and Encoding: Use a Sentence Transformers model fine-tuned for semantic similarity, such as
paraphrase-MiniLM-L6-v2orall-mpnet-base-v2. These models map sentences to embeddings where similar sentences are closer in vector space. For a corpus of 10,000 sentences, encoding might take minutes on a CPU or seconds on a GPU. Preprocess text by normalizing case and punctuation, but avoid aggressive cleaning to preserve meaning.Efficient Similarity Search: Directly comparing every pair of embeddings (10,000² = 100M comparisons) is impractical. Instead, use libraries like FAISS or Annoy to build an index for approximate nearest neighbor search. FAISS's
IndexFlatIPwith cosine similarity allows batch queries—for example, finding the top 5 most similar sentences for each input in milliseconds. Set a similarity threshold (e.g., 0.85) to filter weak matches.Post-Processing and Validation: After mining candidate pairs, deduplicate results by keeping only mutual top matches (if sentence A's top match is B, and B's top match is A). For critical applications, add a cross-verification step using models like BERT for natural language inference (NLI) to check entailment. Store results in a lookup table or graph structure for efficient retrieval.
Example: For a FAQ system with 50k entries, this pipeline could reduce 50,000² comparisons to 50,000 FAISS queries, identifying clusters like "reset password" and "how to change login" as duplicates. Threshold tuning on a validation set (e.g., 100 manually labeled pairs) ensures precision/recall balance.