← All models · mBERT language detection
use case · practical
Language identification is the triage step before everything else. Here a batch of incoming support messages is detected line-by-line and routed to a per-language queue — exactly what you'd do to hand a ticket to the right team, translator, or moderation model. Paste your own messages (one per line).
Each message lands in a queue for its detected language; low-confidence ones are flagged for a human. In a real desk, the queue is where you'd attach the right translator or auto-responder — and because detection runs on-device, no customer text leaves the browser to get there.
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