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PII Lens

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.

  • Status available
  • License CC-BY-4.0
  • Version 1.0.0
  • Updated 2026-06-17
  • PhEye compatibility >=1.0.0
  • Languages en
  • Model size 1.7 GB
  • Author Philterd

Entities detected

  • PERSON

When to load this lens

Load this lens for English person-name detection when accuracy matters most and you can spend the compute. It is the highest-capacity size, best suited to GPU-backed server-side use, and 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 (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.
  • 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.

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 highest-capacity member of the ph-eye-pii-en family, fine-tuned from urchade/gliner_large-v2.1 (DeBERTa-v3-large) on the synthetic nvidia/Nemotron-PII dataset. It is built for server-side use where accuracy is the priority and a GPU is available. 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. This model calibrates its confidence higher than the smaller sizes, so a threshold around 0.95 is the recommended operating point; lower it to push recall higher.

When to use this

  • Server-side, GPU-backed pipelines where accuracy outweighs latency and footprint.
  • Workloads with harder or more varied name forms than the smaller sizes handle well.
  • 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.
  • Largest size. Higher memory and latency needs than the small and medium lenses; size it to your serving budget. Run it at its recommended threshold (around 0.95): at lower thresholds its precision drops sharply.
  • 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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