A compact, fast embedding specialist trained specifically on paraphrase detection — it excels at recognizing when two pieces of text mean the same thing even when worded differently. Its small size (6 layers, ~22M parameters) makes it snappy to run even on modest hardware, though it trades depth for speed and won't capture the nuance that larger models handle. Think of it as the colleague who quickly spots duplicate questions in a support queue.