How would you use Sentence Transformers for an application like plagiarism detection or finding highly similar documents?

To use Sentence Transformers for plagiarism detection or finding similar documents, you would leverage their ability to convert text into dense vector embeddings that capture semantic meaning. These embeddings allow you to compare documents by measuring the similarity between their vectors. For example, a model like all-mpnet-base-v2 generates 768-dimensional vectors where semantically similar sentences or paragraphs are positioned closer in the vector space. By calculating cosine similarity between embeddings, you can identify documents with high similarity scores, which may indicate plagiarism or content overlap. This approach works even when text is paraphrased, as the model focuses on meaning rather than exact word matches.

The process involves three steps. First, preprocess documents by splitting them into chunks (e.g., sentences or paragraphs) to handle large texts. Next, generate embeddings for each chunk using a pretrained Sentence Transformer model. For example, using the Python sentence-transformers library, you could load paraphrase-distilroberta-base-v1 and encode text with model.encode(text_chunks). Finally, compute pairwise similarity scores between embeddings of a source document and a repository of target documents. If a chunk from Document A has a similarity score above a threshold (e.g., 0.85) with a chunk from Document B, it flags potential plagiarism. For efficiency, you’d use vector databases like FAISS or Annoy to index embeddings and enable fast similarity searches across large datasets.

Key considerations include scalability and accuracy. For large-scale applications, indexing millions of embeddings requires tools like FAISS to reduce search time from hours to milliseconds. You might also fine-tune the model on domain-specific data (e.g., academic papers) to improve relevance for niche topics. However, false positives can occur with common phrases (e.g., “the results show”), so combining semantic similarity with traditional methods like n-gram overlap checks adds robustness. For example, a hybrid system might first filter documents using embeddings, then apply stricter lexical analysis to top matches. This balances speed and precision, making it practical for real-world use cases like academic integrity checks or news article duplication detection.