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PII Lens

English Names (Small)

Low-latency English person-name detector, fine-tuned from GLiNER small on NVIDIA Nemotron-PII. The on-device size in the ph-eye-pii-en family; Phileas handles structured identifiers via its pattern-based layer.

  • Status available
  • License CC-BY-4.0
  • Version 1.0.0
  • Updated 2026-06-17
  • PhEye compatibility >=1.0.0
  • Languages en
  • Model size 580 MB
  • Author Philterd

Entities detected

  • PERSON

When to load this lens

Load this lens for fast, low-footprint person-name detection in English text, on a CPU or at the edge. It is a focused PERSON detector; emails, phone numbers, IDs, and other structured PII are handled by Phileas's pattern-based detection, not this model.

Pairs well with

  • English Names (Medium): Mid-size English person-name detector, fine-tuned from GLiNER medium on NVIDIA Nemotron-PII. The recommended default in the ph-eye-pii-en family; Phileas handles structured identifiers via its pattern-based layer.
  • English Names (Large): Highest-capacity English person-name detector, fine-tuned from GLiNER large on NVIDIA Nemotron-PII. The server-side size in the ph-eye-pii-en family; Phileas handles structured identifiers via its pattern-based layer.

What this lens detects

  • PERSON: people’s names as they appear in English text.

This is a name-only lens. Emails, phone numbers, SSNs, credit cards, IP addresses, and other structured PII follow regular patterns and are detected by Phileas’s pattern-based (regex, checksum, and dictionary) layer, not by this model. Compose this lens with that layer for full coverage.

Why this lens

This is the small, low-latency member of the ph-eye-pii-en family, fine-tuned from urchade/gliner_small-v2.1 (DeBERTa-v3-small) on the synthetic nvidia/Nemotron-PII dataset. It is built for tight latency and footprint budgets: CPU-friendly inference and the smallest on-disk size of the three sizes. Like its siblings it is recall-leaning by design, since in redaction a missed name is a leak while an extra span is only over-redaction. A confidence threshold around 0.90 is a sensible starting operating point; lower it to push recall higher.

When to use this

  • On-device or edge deployments where latency and memory are constrained.
  • High-throughput pipelines that run on CPU without a GPU.
  • As the English name detector composed with Phileas’s pattern-based detection for structured PII.

Known limitations

  • Names only. This lens detects PERSON. Other PII is handled by Phileas’s pattern-based detection; compose accordingly.
  • English only. For other languages, load the corresponding language lens when available.
  • Trained on synthetic data. Reported accuracy is in-distribution on Nemotron-PII and is a ceiling, not a production guarantee; validate precision and recall on your own text. The model is recall-leaning, so expect some over-redaction and tune the threshold to your precision/recall balance.
  • The underlying model is licensed CC-BY-4.0; the Nemotron-PII training data requires attribution to NVIDIA.

Use this lens with PhEye, Phileas, or Philter

PhEye loads this lens at configuration time and exposes it to Phileas and Philter automatically. Have questions about a specific deployment? Talk to the team.

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