nobody-pii-de v0.3.0

Local German PII detection for document redaction, privacy review, and pseudonymization workflows.

nobody-pii-de is a German-first multilingual GLiNER model for finding names, postal addresses, dates of birth, and organizations in German, English, and Dutch business text. The full nobody pipeline adds checksum- and format-aware local detectors for email addresses, phone numbers, IBANs, credit cards, German tax IDs, Dutch BSNs, license plates, and numeric date formats.

The model and pipeline run locally. They are useful starting points for document intake, support exports, CRM cleanup, archive migration, and privacy review. They are not legal-compliance or anonymization guarantees; evaluate leakage on representative documents before production use.

Release identity

v0.3.0 is a production-policy release; the binary model is unchanged from v0.2.0. The new source pipeline reduces measured German token leakage through better building-number, date, phone, person-boundary, and address normalization. Use the v0.3.0 source tag for the metrics on this card.

The binary files were verified at Hub revision 311329838bba75d974be36b042af7836f99b30fa. Card and attribution metadata may receive later commits; pin that revision when the exact binary artifact is required.

file	SHA-256
model.safetensors	84cb4207ec386a2467894328bccd83127ac62ee771779c0c909047ea3cc54d06
gliner_config.json	16adf9e94687869a82af3234fce29550096df7ccfeb0438e957cf89d358337ec
tokenizer.json	c5b8041501fcdee792b9b112dd592861f04391a24633a73fd9f05aaaf6e8eff1
tokenizer_config.json	d5fc9ee06444205f6d6e9cc1b0ba05855ca740246d4c4dbabbd15bfc1e0170b7

The starting checkpoint was urchade/gliner_multi_pii-v1 at revision 1fcf13e85f4eef5394e1fcd406cf2ca9ea82351d.

Intended use

Use this artifact to identify potentially sensitive entities before applying a redaction or human-review policy. The model layer predicts:

• person
• address
• date of birth
• organization

The companion pipeline additionally detects structured labels such as email, phone number, IBAN, credit card, national ID, and license plate with syntax- or checksum-aware rules.

Suitable uses

• pre-screening German business documents for privacy review;
• local or offline PII masking and pseudonymization pipelines;
• CRM, support, archive, and document-migration cleanup;
• research on recall-first multilingual PII detection.

Out-of-scope uses

• claiming that output is legally anonymized;
• making decisions about people from detected attributes;
• processing documents without an appropriate security and data-governance boundary;
• relying on benchmark scores without an evaluation from the intended domain.

Usage

Complete redaction pipeline

This is the evaluated and recommended path:

git clone https://github.com/naeyn/nobody.git
cd nobody
git checkout v0.3.0
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python redact.py --model naeyn/nobody-pii-de \
  --text "Kontakt: Anna Müller, anna@example.de"

GLiNER model layer only

from gliner import GLiNER

model = GLiNER.from_pretrained("naeyn/nobody-pii-de")
text = "Kontakt: Anna Müller, geboren am 5. Juni 1984."
entities = model.predict_entities(
    text,
    ["person", "address", "date of birth", "organization"],
    threshold=0.5,
)
print(entities)

Direct predict_entities calls do not include the deterministic structured- identifier detectors or boundary normalization used for the reported production metrics.

The released model thresholds are person=0.50, address=0.05, date of birth=0.50, and organization=0.50. The deterministic layer runs independently of model confidence.

Training recipe

The unchanged model checkpoint was fine-tuned from the pinned GLiNER base with a mixed training set of 38,581 document instances:

• 25,909 German rows from ai4privacy/pii-masking-openpii-1m, selected from the German training source and excluding every row whose raw labels contained DATE. The publisher states that OpenPII-1M is synthetic; its generating model is not disclosed.
• 12,672 instances from the companion synthetic corpus: all 9,450 training documents plus one additional copy of each of its 3,222 documents containing at least one date of birth span. This is a 2× oversample of DOB-bearing documents, not a second independently generated split.

The mixed file was concatenated and shuffled with seed 42; the trainer also used seed 42. The generated corpus's generation seed was not retained in its published snapshot, so the training seed must not be mistaken for a data- generation seed.

setting	value
epochs	1
selected global step	800
per-device batch size	4
gradient accumulation	4 (effective batch size 16)
GLiNER learning rate	1.23465e-05
other-parameter learning rate	7.48573e-05
weight decay	0.01 for both parameter groups
scheduler	linear
warmup	10% of training steps
training seed	42
checkpoint interval	400 steps

Checkpoint 800 was selected on a separate German development stream before the canonical evaluation. Neither canonical benchmark nor the synthetic test split was used for checkpoint selection.

Only inference artifacts are published here: weights, tokenizer, model configuration, license, notice, and this card. Optimizer state, training logs, restricted benchmark records, and private orchestration are not included.

Evaluation methodology

Every result below uses the full v0.3.0 production pipeline: deterministic detectors, one model pass, fixed per-label thresholds, span normalization, and one label mapping. Leakage is the percentage of gold PII tokens missed by all predicted PII spans; lower is better. F1 requires exact token-span matches.

The seven-loop release process used:

1. disjoint development-only selection;
2. a written finalist and gate rule before one frozen canonical look per loop;
3. fixed production thresholds rather than per-benchmark tuning;
4. exact-text and n-gram contamination checks with planted-positive controls;
5. paired document-level bootstrap differences with 10,000 resamples;
6. explicit reporting of failed gates and negative experiments.

Shipping required German F1 ≥ matched zero-shot F1 + 0.05, ≤2.00% token leakage on both German and synthetic sets, and minimum cross-set F1 ≥0.55. Loop 7 added a stricter rule that the German leakage interval's upper bound must also remain below 2.00%.

The complete public research record is in RESEARCH.md.

Results

External German benchmark

The external evaluation uses 3,000 documents and 1,624 labeled spans from the German validation stream of ai4privacy/pii-masking-400k. Its publisher describes it as synthetic. It is external to nobody's training distribution, was not included in released weights, and is not redistributed here. Intervals are 95% document-level bootstrap intervals.

model	exact-span F1	token leakage
nobody v0.3.0 pipeline	0.795 [0.780–0.810]	1.51% [1.079–1.995]
Piiranha-v1, matched v0.3.0 pipeline	0.840	0.93%
gliner_multi_pii-v1 zero-shot, matched pipeline	0.635	2.26%

Against Piiranha on this release, paired ΔF1 is −0.0452 [−0.0586, −0.0318] and paired Δleakage is +0.58pp [+0.11, +1.09]. nobody does not beat Piiranha on Piiranha's home release.

Per-label nobody results:

label	precision	recall	F1	token leakage
person	0.795	0.976	0.876	0.62%
address	0.481	0.687	0.565	15.55%
email	0.987	0.987	0.987	0.00%
phone number	0.901	0.986	0.942	0.69%
date of birth	0.305	0.993	0.466	0.45%
total	0.686	0.946	0.795	1.51%

The 1.995% upper confidence bound passes the stricter loop-7 leakage gate by only 0.005 percentage points. P(leakage ≤ 2.0%) across bootstrap resamples was 0.976. This is a thin pass, not a comfortable safety margin.

Synthetic multilingual regression set

The held-out companion test split covers German, English, and Dutch and contains 3,426 spans:

model	exact-span F1	token leakage
nobody v0.3.0 pipeline	0.927	0.51%
gliner_multi_pii-v1 zero-shot	0.826	6.60%
Piiranha-v1	0.677	24.09%

This is regression evidence on a procedurally generated, template-based set, not independent real-world evidence. Piiranha is outside its training distribution here.

Cross-release comparison

A contamination-filtered 3,000-document sample from the CC-BY-4.0 OpenPII-1M German validation release reverses the ranking:

benchmark	nobody	Piiranha	paired ΔF1
400k validation, Piiranha's release	0.795	0.840	−0.045 [−0.059, −0.032]
OpenPII-1M validation, nobody's release	0.982	0.915	+0.067 [+0.059, +0.074]

The OpenPII-1M benchmark excluded 590 documents sharing a 12-gram with training; no exact duplicates remained. This table is not a universal nobody win: each model leads on its home generator, OpenPII-1M has no DOB class, and address gold conventions differ. The ≈0.105 F1 interaction indicates that synthetic exact-span benchmarks measure generator and annotation conventions as well as PII detection capability.

v0.3.0 change at identical weights

Against the loop-6 single-model champion:

• paired ΔF1: −0.0064 [−0.0097, −0.0034];
• paired Δleakage: −0.54pp [−0.96, −0.20].

Both effects exclude zero. The release deliberately traded a small F1 loss for a larger leakage reduction. German per-class leakage moved from 2.71% to 0.45% for DOB and from 1.49% to 0.69% for phone numbers; address remains the dominant residual at 15.55%.

Training data and provenance

The model has two training sources:

1. naeyn/nobody-pii-synth-de, using its 9,450-document generated training split. Documents are procedurally generated and labeled by construction.
2. 25,909 German rows from ai4privacy/pii-masking-openpii-1m, used under CC-BY-4.0. Rows whose raw labels contained DATE were excluded because that taxonomy does not distinguish ordinary dates from dates of birth.

The evaluation-only 400k stream was not included in released weights. No real personal records are intentionally included in the published companion dataset or model inputs. OpenPII's publisher describes its data as synthetic but does not disclose its generator.

Limitations and safety

• Address is the weakest class: 0.565 F1 and 15.55% token leakage on the external German benchmark. Bare house numbers and context-free street or place fragments can be missed.
• DOB matching is recall-first: recall 0.993 and precision 0.305. Ordinary dates can be over-redacted.
• The leakage CI narrowly clears the budget: the upper bound is 1.995%, only 0.005pp below 2.00%.
• All reported evaluations are synthetic. Performance on scans, handwriting, unseen OCR systems, or another organization's documents is unknown.
• Exact-span metrics are sensitive to annotation conventions, as the two-release comparison demonstrates.
• Redaction is pseudonymization, not necessarily anonymization; surrounding context can still permit re-identification.
• Keep source documents and redaction logs inside an appropriate security boundary. Do not send sensitive documents to third-party services without a processing agreement and security review.

Reproducibility status

Inference is reproducible from the pinned Hub revision, file checksums, and the public v0.3.0 source tag. The public source includes the released behavior tests, methodology, changelog, and aggregate evidence. Restricted benchmark records and private orchestration are not published, so external German aggregate scores cannot be independently rerun from the public repositories alone. The companion synthetic test remains regression evidence, not a substitute for a domain- representative evaluation.

License and attribution

Released weights are Apache-2.0. The base model urchade/gliner_multi_pii-v1 is Apache-2.0.

The OpenPII-1M portion of the mix is used under CC-BY-4.0. Required attribution: Ai4Privacy / Ai Suisse SA. See the CC-BY-4.0 license and OpenPII-1M dataset card. The publisher describes that source as synthetic and does not disclose its generating model.

The companion synthetic dataset is Apache-2.0. Its values were created with Faker locale providers; Faker is MIT-licensed. See its dataset card for details. Piiranha is CC-BY-NC-ND and is used only as a research comparator.

Citation

@software{nobody_pii_de,
  title = {nobody-pii-de: German-first multilingual PII detection and redaction},
  author = {naeyn},
  year = {2026},
  version = {0.3.0},
  url = {https://huggingface.co/naeyn/nobody-pii-de},
  license = {Apache-2.0}
}