Input is plain text; output is character spans typed against the 18
HIPAA Safe Harbor identifier categories. It is designed to run behind the
Stage 1 rules tier, which supplies checksum-validated spans it cannot beat,
and which suppresses failure classes it re-introduces on its own (see Limitations).
Not a de-identification guarantee. It is a detector that helps a reviewer.
Safe Harbor de-identification is a legal determination this model cannot make.
How good is it?
At the level you act on: does this document contain PII?
configuration
recall
documents missed
false alarms
deep @0.30
0.9980
6 of 2,983
0.214
deep @0.50
0.9977
7 of 2,983
0.000
deep @0.70
0.9963
11 of 2,983
0.000
Measured on 3,000 held-out Nemotron documents; a document counts as
sensitive if it carries a gold span of a type we model. False alarms are measured
on 14 adversarial negatives -- order numbers, chart numbers,
subscriber ids -- not on easy ones.
At the span level: is the tag right?
Exact (type, start, end) match, fused with the rules tier.
F2 weights recall four times as heavily as precision, which is closer to this
system's cost matrix than F1; both are shown.
recall
F2
F1
precision
the 12 types the rules also cover
0.918
0.927
0.940
0.962
the 14 types only this model emits
0.911
0.918
0.930
0.951
Per type
type
gold
recall
F2
F1
precision
CREDIT_CARD
380
0.108
0.129
0.183
0.612
TAX_ID
43
0.349
0.399
0.508
0.938
FAX_NUMBER
211
0.782
0.808
0.851
0.932
DEVICE_ID
80
0.787
0.818
0.869
0.969
GEO_COORDINATE
233
0.897
0.902
0.909
0.921
DATE_TIME
325
0.892
0.907
0.931
0.973
PERSON_NAME
3,019
0.909
0.913
0.918
0.928
ADDRESS
1,568
0.902
0.913
0.931
0.961
US_DRIVER_LICENSE
170
0.912
0.916
0.923
0.934
USER_ID
1,339
0.923
0.932
0.947
0.972
ACCOUNT_NUMBER
510
0.931
0.941
0.956
0.981
VEHICLE_ID
294
0.959
0.954
0.946
0.934
PHONE_US
638
0.981
0.964
0.939
0.899
URL
1,198
0.968
0.967
0.964
0.960
BIOMETRIC_ID
358
0.961
0.967
0.976
0.991
HEALTH_PLAN_ID
311
0.968
0.972
0.979
0.990
BANK_ROUTING
277
0.968
0.972
0.980
0.993
SSN
249
1.000
0.976
0.941
0.889
MRN
389
0.974
0.979
0.986
0.997
IP_ADDRESS
291
0.979
0.981
0.985
0.990
MAC_ADDRESS
137
0.985
0.985
0.985
0.985
SWIFT_BIC
157
0.994
0.987
0.978
0.963
DATE_DOB
403
0.995
0.995
0.994
0.993
EMAIL
1,221
0.998
0.997
0.997
0.997
Limitations that matter
These scores are for Nemotron-PII, and they do NOT generalise. Measured on
ai4privacy/pii-masking-300k -- a different corpus, label space, document style
and locale -- in-scope strict span recall is 0.385 against 0.914 here, and
document-level recall 0.870 against 0.998. Format-anchored types transfer
intact (EMAIL 0.943, IP 0.988); learned semantic types collapse (names and
addresses ~0.30, mostly boundary errors on structured JSON text). Deep mode
still roughly doubles the rules on that corpus, so the cascade earns its place --
but budget for the lower number on text unlike the training set.
Synthetic training data. Nemotron-PII is generated, not real, and the
standard clinical benchmark (n2c2/i2b2 2014) requires a data use agreement and
was not used. No number here describes real clinical text.
The demographic slice is synthetic too. Name recall varies by only 0.020
across race/ethnicity groups, which sounds excellent and mostly reflects names
drawn from a generator rather than from the world. It is a real gate that would
catch a large disparity; it certifies synthetic names only.
The PII-vs-PHI split has no external gold. Nemotron has no document labels
and no medical-context annotation, so that boundary is only scored on a
39-document authored corpus. It is the weakest link in the evaluation.
Credit card numbers are deliberately suppressed. 88% of the training
corpus's cards fail the Luhn checksum, so the serving path re-validates and
drops them. Measured F1 on that type is ~0.18 against this corpus, and that is
correct behaviour rather than a defect.
US / English scope. Non-US identifier formats are out of scope.
Calibrated for THIS model on THIS corpus. A threshold tuned here is not
automatically right for another text distribution.
It can be confidently wrong. Adversarial near-misses are its documented
failure class, which is why the serving path ships a confidence floor, checksum
re-validation and rule fusion. Do not run it bare.
Licensing
The code is MIT (see LICENSE). The model is a derivative work of its training
data, and inherits obligations from every corpus below.
nvidia/Nemotron-PII — CC BY 4.0; attribution to NVIDIA is required when
redistributing this model or its outputs.
The weights ship in model.onnx.data beside model.onnx. A package whose weights
are corrupted without changing their size still loads and still answers: on the
real artifact a flipped kilobyte turned an MRN into a USER_ID at 0.87
confidence, which is a silent PHI miss. Only the checksum catches that.
