On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidence
Named-entity recognition (NER) is increasingly wanted on-device (no API, low latency, data kept local). The practitioner's question is not the leaderboard but which model is deployable, how to evaluate it without human annotation, and whether its confidence can be trusted. We answer these jointly. We place nine systems across three paradigms and 13 M to 8 B parameters: a classical tagger (spaCy), bidirectional-encoder specialists (GLiNER, 166 to 460 M), and generative LLMs run locally (Qwen3-0.6B/1.7B/4B-Instruct, DeepSeek-R1-1.5B/8B), on three datasets of differing character, and report accuracy plus two axes the literature omits: latency and output validity. Because our corpus (RSS-News...
| Authors | Vinay Kumar Chaganti |
|---|---|
| Primary category | cs.CL |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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