Sentence Transformers can efficiently identify duplicate or overlapping text entries by converting text into semantic embeddings and measuring their similarity. These models generate dense vector representations that capture the meaning of sentences, allowing you to detect redundancy even when wording differs. Here's how to approach deduplication:
Step 1: Generate Embeddings
First, use a pre-trained Sentence Transformer model (e.g., all-MiniLM-L6-v2) to encode all text entries into fixed-length vectors. Each vector represents the semantic content of a text entry. For example, the sentences "How to reset your password" and "Steps to recover your login credentials" would produce similar embeddings despite differing words. Batch processing and GPU acceleration can speed up encoding for large datasets.
Step 2: Efficient Similarity Search Instead of comparing every pair of embeddings (which is computationally expensive for large datasets), use approximate nearest neighbor (ANN) libraries like FAISS or Annoy. These tools index embeddings to quickly find entries with vectors close to each other in the semantic space. For instance, you might configure FAISS to return all entries with a cosine similarity score above 0.9, which indicates near-identical meaning.
Step 3: Thresholding and Clustering Define a similarity threshold to flag duplicates. This threshold depends on your data: Lower values (e.g., 0.8) work for loosely related content, while higher values (e.g., 0.95) catch strict duplicates. Optionally, cluster entries using algorithms like DBSCAN to group all related duplicates in one pass. For example, product descriptions like "Wireless headphones with 20hr battery" and "20-hour Bluetooth earphones" would cluster together, allowing you to retain only one version.
Practical Considerations
- Preprocess text minimally (e.g., trim whitespace, lowercase) since Sentence Transformers handle punctuation and casing well.
- Test thresholds on a subset of data to balance precision (avoid false duplicates) and recall (catch all redundancies).
- For scalability, pair ANN with distributed computing frameworks like Spark for datasets exceeding memory limits.
This approach outperforms exact string matching by addressing paraphrased or reordered content while scaling to millions of entries with tools like FAISS.