To use embeddings for duplicate detection, you first convert your data into vector representations (embeddings) and then measure their similarity. Embeddings are numerical vectors that capture semantic features of your data, whether it's text, images, or other formats. When two items are duplicates or near-duplicates, their embeddings will typically be close to each other in the vector space. By calculating the distance or similarity between these vectors, you can identify duplicates. This approach works well because embeddings preserve meaningful patterns in the data, allowing you to detect similarities even when the raw data isn't identical.
The process involves three main steps: generating embeddings, measuring similarity, and setting a threshold. For text data, you might use a pre-trained model like BERT, Sentence-BERT, or Universal Sentence Encoder to convert sentences or documents into vectors. For example, the sentence "How to reset your password" and "Steps to recover your login credentials" might have very similar embeddings even though the wording differs. Once embeddings are generated, you calculate similarity using metrics like cosine similarity (which measures the angle between vectors) or Euclidean distance (which measures straight-line distance). A cosine similarity score above 0.9, for instance, might indicate potential duplicates. You’ll need to test different thresholds based on your data—for example, product descriptions might require stricter thresholds than social media posts.
Practical implementation involves tools and optimizations. Libraries like Sentence Transformers simplify embedding generation in Python. Here’s a basic workflow: use SentenceTransformer('all-MiniLM-L6-v2') to create embeddings for your text, then compute pairwise similarities with sklearn.metrics.pairwise.cosine_similarity. For large datasets, comparing every pair directly becomes computationally expensive, so approximate methods like FAISS or Annoy can speed up nearest-neighbor searches. Preprocessing steps like lowercasing, removing stopwords, or handling special characters can improve embedding quality. For instance, cleaning "iPhone12" to "iphone 12" ensures consistency. If you’re working with code snippets, tools like code2vec can generate embeddings that capture structural similarities. Testing with a labeled dataset helps refine your threshold and model choice—iterating based on precision (avoiding false duplicates) and recall (catching true duplicates).