How can Sentence Transformers be used for data deduplication when you have a large set of text entries that might be redundant or overlapping?

Sentence Transformers can efficiently identify and remove redundant text entries in large datasets by converting text into numerical representations (embeddings) and measuring their semantic similarity. These models, trained to capture contextual meaning, map sentences to dense vectors where similar content clusters closer in vector space. To deduplicate data, embeddings for all text entries are generated first. Redundant entries are then detected by comparing vector distances—closer vectors indicate higher similarity. Approximate nearest neighbor (ANN) libraries like FAISS or Annoy are often used to scale this process, avoiding exhaustive pairwise comparisons. Finally, entries exceeding a similarity threshold are flagged as duplicates and removed or merged.

The process involves three main steps. First, generate embeddings for all text entries using a pre-trained Sentence Transformer model like all-MiniLM-L6-v2. For example, a dataset of product descriptions might be converted into 384-dimensional vectors. Second, compute pairwise similarities between embeddings using cosine similarity or Euclidean distance. To handle large datasets efficiently, ANN techniques reduce computation time by indexing embeddings and querying for nearest neighbors. For instance, FAISS can quickly find the top 10 most similar entries for each item. Third, apply a similarity threshold (e.g., 0.95) to classify entries as duplicates. Texts like “Fast charging USB-C cable” and “USB-C cable with rapid charging” might score 0.98 and be merged, while a score of 0.85 might indicate distinct entries. Clustering algorithms like DBSCAN can also group similar items for batch processing.

Practical considerations include balancing precision and recall. A high threshold (e.g., 0.97) reduces false positives but might miss paraphrased duplicates, while a lower threshold (e.g., 0.85) risks merging unrelated entries. Testing thresholds on a sample dataset helps optimize this trade-off. Additionally, preprocessing steps like lowercase conversion or removing special characters improve consistency. For scalability, embedding generation can be parallelized using GPUs, and ANN libraries support distributed computing. Post-processing might involve manual review of borderline cases or retaining the longest/shortest entry in a duplicate group. Tools like sentence-transformers, FAISS, and scikit-learn provide ready-to-use implementations, making this approach accessible to developers without deep ML expertise.