To perform paraphrase mining using Sentence Transformers, you first encode sentences into dense vector representations (embeddings) and then compare these embeddings to identify semantically similar or duplicate sentences. Sentence Transformers, such as the paraphrase-MiniLM-L6-v2 model, are specifically trained to capture semantic meaning, making them effective for this task. The process involves three main steps: embedding generation, similarity calculation, and threshold-based filtering. By converting text to numerical vectors, you can efficiently measure similarity using metrics like cosine similarity, which quantifies how “aligned” two vectors are in a high-dimensional space.
The technical implementation starts by embedding all sentences in your corpus. For example, using the sentence-transformers library in Python, you load a pre-trained model and encode sentences into embeddings. Here’s a simplified code snippet:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
embeddings = model.encode(sentences, batch_size=128, show_progress_bar=True)
For large datasets (e.g., millions of sentences), direct pairwise comparison of all embeddings is computationally expensive. Instead, approximate nearest neighbor (ANN) libraries like FAISS or Annoy are used to speed up similarity searches. These tools index embeddings in a way that allows fast retrieval of the closest matches. For instance, FAISS can reduce search time from hours to seconds by clustering vectors or using quantization. After indexing, you query each sentence’s embedding against the index to find its nearest neighbors, filtering results by a similarity threshold (e.g., 0.85 cosine similarity) to flag potential paraphrases.
Practical considerations include choosing the right model, threshold tuning, and handling scalability. Models like paraphrase-MiniLM-L6-v2 balance speed and accuracy, but larger models (e.g., all-mpnet-base-v2) may offer better accuracy at the cost of compute resources. Thresholds depend on your use case: a lower threshold (0.7) captures more paraphrases but risks false positives, while a higher threshold (0.9) ensures precision but may miss subtle similarities. For example, in a customer support corpus, sentences like “I can’t log into my account” and “My login isn’t working” might score 0.92, indicating a duplicate. Testing thresholds on a labeled subset of your data ensures optimal results. Finally, batch processing and distributed computing frameworks (e.g., Dask) can help manage memory and processing time for very large datasets.