⚠️ Research-only / non-commercial. This variant of gheim-ch-560m was fine-tuned on a multi-source training mix that includes Babelscape WikiNeural (CC BY-NC-SA 4.0) and CoNLL-2003 (Reuters Corpus, research-only). The most restrictive upstream licence binds the released model: the checkpoint may not be used for commercial purposes. For commercial deployments use the sister checkpoint joelbarmettler/gheim-ch-560m, which is trained only on the Apache-2.0-compatible gheim-ch-pii-212k dataset.

A multilingual token-classification model for personally-identifiable information (PII) detection across the four official Swiss languages (de_CH, fr_CH, it_CH, rm) and English. Architecture and parameter count are identical to the flagship joelbarmettler/gheim-ch-560m; the difference is the training mix, which adds public external NER / PII training data on top of the in-domain gheim-ch-pii-212k corpus to lift cross-domain transfer (especially on Swiss-news text and external person-NER benchmarks).


Parameters	560M
Languages	de_CH, fr_CH, it_CH, rm, en
Categories	account_number, private_address, private_date, private_email, private_person, private_phone, private_url, secret
Tag scheme	BIOES (33 classes)
Max sequence length	512
License	CC BY-NC-SA 4.0 (with Reuters research-only restriction inherited from CoNLL-2003)

When to choose this variant

If your use case is…	Use
Production deployment, customer data, commercial product	`joelbarmettler/gheim-ch-560m` (Apache 2.0)
Academic research, benchmarks, papers, evaluations	this checkpoint
Cross-domain redaction (Swiss-news articles, English news, multilingual Wikipedia)	this checkpoint (substantially stronger on those distributions)
In-domain redaction (Swiss court / parliament / web text)	either — numbers are essentially identical (see below)

Performance vs the commercial flagship

In-distribution (held-out test split of gheim-ch-pii-212k, 21,246 chunks) — the two checkpoints are within 0.2 pp on every headline metric:

Metric	`gheim-ch-560m` (Apache 2.0)	`gheim-ch-560m-research` (this)	Δ
Test strict F1	0.910	0.912	+0.002
Test char F1	0.946	0.946	0.000
Per-(lang × cat) char F1	0.940	0.940	0.000

On six external benchmarks (PER char F1). Three of the six — openpii-1m, WikiNeural, CoNLL-2003 — are present in this variant's training mix, so those numbers are in-distribution generalisation, not zero-shot cross-domain transfer. The swissner, open-pii-500k, and gretel_finance rows are zero-shot for both checkpoints and are the honest cross-domain comparison.

Benchmark	n	gheim-ch-560m (Apache 2.0)	gheim-ch-560m-research (this)	Δ
`ZurichNLP/swissner` (Swiss-news NER, zero-shot for both)	800	0.702	0.903	+20.1 pp
`ai4privacy/pii-masking-openpii-1m` (in-distribution for this variant)	8,000	0.938	0.995	+5.7 pp
`ai4privacy/open-pii-masking-500k` (zero-shot for both)	8,000	0.933	0.982	+4.9 pp
`gretelai/synthetic_pii_finance_multilingual` (zero-shot for both)	4,800	0.624	0.627	+0.3 pp
`Babelscape/wikineural` (in-distribution for this variant)	8,000	0.808	0.795	−1.3 pp
`tomaarsen/conll2003` (in-distribution for this variant)	3,453	0.911	0.765	−14.6 pp

Headline finding: on the zero-shot Swiss-news test (swissner), this variant attains 0.903 PER char F1 vs the Apache-2.0 baseline's 0.702 — a 20 pp gain from broader training data even though swissner itself is not seen at training time (no swissner train split exists; the gain comes from openpii + WikiNeural + CoNLL-2003 generalisation).

Two caveats worth understanding before using this variant:

On Babelscape/WikiNeural and tomaarsen/conll2003 — both present in this variant's training mix — the research variant actually regresses against the Apache-2.0 baseline. The broader 8-category output schema produces non-PER false positives on news / Wikipedia text (where any IBAN-shaped or email-shaped string would be flagged) that the in-domain-only Apache baseline does not produce. PII-detector recall comes at the cost of NER-benchmark precision.
On gretel_finance the two variants are statistically tied (~0.62). None of the training mixes contain financial-document text, so this benchmark measures the architecture's structural-PII generalisation. Adding financial-domain data is a planned future iteration.

Per-language swissner PER char F1 (the headline cross-domain finding):

Language	Apache 2.0	Research	Δ
de	0.539	0.931	+39 pp
fr	0.761	0.913	+15 pp
it	0.643	0.856	+21 pp
rm	0.409	0.873	+46 pp

The Romansh result is the most notable: no Romansh training data is added (none of the external corpora include RM), but the model gains 46 pp on Romansh swissner PER char F1 via cross-lingual transfer in XLM-R's shared encoder body from the additional de/fr/it/en supervision.

Per-(language × category) char-level F1 on the in-domain test

Category	de_ch	fr_ch	it_ch	rm	en	Avg.
`account_number`	0.994	0.998	0.992	0.971	1.000	0.995
`private_address`	0.932	0.912	0.920	0.851	0.962	0.917
`private_date`	0.949	0.909	0.951	0.919	0.899	0.933
`private_email`	0.996	0.998	0.999	0.991	1.000	0.996
`private_person`	0.913	0.939	0.952	0.907	0.959	0.930
`private_phone`	0.995	0.998	0.997	0.987	1.000	0.995
`private_url`	0.991	0.994	0.993	0.993	0.967	0.991
`secret`	0.993	0.999	1.000	1.000	n/a	0.997
Avg.	0.943	0.931	0.956	0.916	0.959	0.940

Usage

The serving surface is identical to the Apache-2.0 variant; the SDKs and transformers / transformers.js pipelines accept either repo ID. Swap the model ID:

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

repo = "joelbarmettler/gheim-ch-560m-research"   # research variant
tok = AutoTokenizer.from_pretrained(repo)
mdl = AutoModelForTokenClassification.from_pretrained(repo)
ner = pipeline("token-classification", model=mdl, tokenizer=tok,
               aggregation_strategy="simple")

For the gheim SDKs:

python

from gheim import Anonymizer
a = Anonymizer(detector_model="joelbarmettler/gheim-ch-560m-research")

Training procedure

Same architecture (FacebookAI/xlm-roberta-large, 560M params), same hyperparameters as the flagship checkpoint (AdamW, LR 5e-5, cosine with 5% warmup, no LLRD, effective batch 128 on 2× RTX 4090 DDP, bf16, 3 epochs, max sequence length 512). Best checkpoint at step 5,500 of 5,946 (epoch 2.77) by validation overall_f1 0.911. Wall time ≈ 94 min train + 5 min eval.

The training data is the union of:

Source	Train chunks	License	Role
`joelbarmettler/gheim-ch-pii-212k` (train split)	170,001	CC BY 4.0	In-domain Swiss PII (court, parliament, web, Romansh)
`ai4privacy/pii-masking-openpii-1m` (train split, de/fr/it/en)	40,000	Apache 2.0 / CC-BY 4.0	Multi-category PII supervision (chat / forms)
`Babelscape/wikineural` (train splits, de/fr/it/en)	39,223	CC BY-NC-SA 4.0	News / Wikipedia PER supervision
`tomaarsen/conll2003` (train split, en, PER-bearing)	4,373	Research-only (Reuters)	English news PER supervision
Total	253,597	(most restrictive binds)

Only chunks containing at least one in-schema PII span are kept from the external sources, so the model receives positive supervision without "implicit O" negatives on text the source dataset did not annotate exhaustively.

The full builder is at training/src/gheim_training/data/external_train.py and the train-mix HF DatasetDict builder is at training/src/gheim_training/data/build_hf_multisource.py.

Limitations

Non-commercial use only. Inherits the most restrictive of its training-data licenses (CC BY-NC-SA 4.0 from WikiNeural; Reuters research-only from CoNLL-2003). If you need a commercial-friendly checkpoint, use joelbarmettler/gheim-ch-560m (Apache 2.0, trained only on in-domain Swiss text; cross-domain numbers are weaker — see the comparison table above).
private_address test strict F1 is 0.84 (char F1 0.92). Boundary placement on multi-token addresses is the dominant error mode.
Swiss German dialect (GSW) is not measured. The fasttext detector used in data preparation labels GSW as standard German.
account_number test strict F1 is 0.99 in the headline, but regex-shaped non-PII in numeric tables can still slip through. For production use, pair with the regex front-end documented in the gheim library (checksum validation: IBAN, AHV, VAT-CHE, Luhn).
Re-identification is not in scope. The model is intended for redaction; it does not return entity-linked identifiers.

License

Composite, bounded by the most restrictive upstream:

Model weights: CC BY-NC-SA 4.0 with a Reuters research-only rider inherited from CoNLL-2003.
Base architecture (FacebookAI/xlm-roberta-large): Apache 2.0 (does not bind the fine-tune since training data overrides).
In-domain training data (gheim-ch-pii-212k): CC BY 4.0.
ai4privacy/openpii-1m training data: Apache 2.0 / CC BY 4.0.
Babelscape/wikineural training data: CC BY-NC-SA 4.0.
tomaarsen/conll2003 training data: Reuters research-only.

If your use case cannot accept research-only / non-commercial terms, use the sibling checkpoint joelbarmettler/gheim-ch-560m.

Citation

bibtex

@misc{barmettler2026gheim_ch_560m_research,
  title  = {gheim-ch-560m-research: A multi-source-trained Swiss-PII detector with strong cross-domain transfer},
  author = {Joel Barmettler},
  year   = {2026},
  url    = {https://huggingface.co/joelbarmettler/gheim-ch-560m-research},
  note   = {Research-only variant of gheim-ch-560m; trained on gheim-ch-pii-212k + ai4privacy/openpii-1m + Babelscape/wikineural + tomaarsen/conll2003}
}

Maintainer

Joel Barmettler · jbarmettler@proton.me · joelbarmettler.xyz · github.com/joelbarmettlerUZH/gheim