OpenMed-PII-SuperClinical-Small-44M-v1
OpenMed-PII-SuperClinical-Small-44M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in text. This model identifies and classifies 54 types of sensitive information including names, addresses, SSNs, medical record numbers, and more.
Params
140 M
Context
512
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Likes
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.6 GB | 1.1 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- DebertaV2ForTokenClassification
- Parameters
- 140 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 128,100
- Layers / heads
- 6 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2026-01-13
- Training datasets
- nvidia/Nemotron-PII
- nvidia/Nemotron-PII (test_strat) (reported)
- 0.9529
- Added to our catalog
- 2026-09-16
Compare with any token-classification model