AI Text Semantic Similarity Calculator
Measure deep semantic relationship and contextual similarity between two passages using vector embeddings.
all-MiniLM-L6-v2100% Client-Side
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Metric: Cosine Similarity of 384-dimensional Dense Vector Embeddings
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Semantic Match Score
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Note on Semantic Similarity:This score represents conceptual/semantic similarity computed via deep neural vector embeddings (`Xenova/all-MiniLM-L6-v2`), not exact string or word matching. Texts with different vocabulary but identical meaning (e.g. "The car is fast" vs "The automobile travels quickly") will yield a high semantic similarity score.
How Sentence Transformer Embeddings Work
Sentence Embeddings
Maps input texts to 384-dimensional dense vector representations capturing semantic context and intent.
Cosine Distance
Calculates normalized dot product between text embedding vectors for precise angular distance matching.
Browser Execution
Executed 100% locally in your web browser. No document contents or sentences are sent over the network.
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