OpenMed-PII-German-mClinicalE5-Large-560M-v1

German PII Detection Model | 560M Parameters | Open Source

Model Description

OpenMed-PII-German-mClinicalE5-Large-560M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in German text. This model identifies and classifies 54 types of sensitive information including names, addresses, social security numbers, medical record numbers, and more.

Key Features

• German-Optimized: Specifically trained on German text for optimal performance
• High Accuracy: Achieves strong F1 scores across diverse PII categories
• Comprehensive Coverage: Detects 55+ entity types spanning personal, financial, medical, and contact information
• Privacy-Focused: Designed for de-identification and compliance with GDPR and other privacy regulations
• Production-Ready: Optimized for real-world text processing pipelines

Performance

Evaluated on the German subset of AI4Privacy dataset:

Metric	Score
Micro F1	0.9617
Precision	0.9582
Recall	0.9653
Macro F1	0.9458
Weighted F1	0.9599
Accuracy	0.9940

Top 10 German PII Models

Rank	Model	F1	Precision	Recall
1	OpenMed-PII-German-SuperClinical-Large-434M-v1	0.9761	0.9744	0.9778
2	OpenMed-PII-German-SnowflakeMed-Large-568M-v1	0.9724	0.9705	0.9743
3	OpenMed-PII-German-ClinicalBGE-568M-v1	0.9724	0.9702	0.9745
4	OpenMed-PII-German-BigMed-Large-560M-v1	0.9714	0.9696	0.9732
5	OpenMed-PII-German-NomicMed-Large-395M-v1	0.9713	0.9690	0.9735
6	OpenMed-PII-German-SuperMedical-Large-355M-v1	0.9701	0.9684	0.9719
7	OpenMed-PII-German-EuroMed-210M-v1	0.9683	0.9667	0.9699
8	OpenMed-PII-German-ClinicalBGE-Large-335M-v1	0.9652	0.9624	0.9680
9	OpenMed-PII-German-ClinicalE5-Large-335M-v1	0.9646	0.9620	0.9672
10	OpenMed-PII-German-BiomedELECTRA-Large-335M-v1	0.9638	0.9598	0.9677

Supported Entity Types

This model detects 54 PII entity types organized into categories:

Identifiers (22 types)

Entity	Description
ACCOUNTNAME	Accountname
BANKACCOUNT	Bankaccount
BIC	Bic
BITCOINADDRESS	Bitcoinaddress
CREDITCARD	Creditcard
CREDITCARDISSUER	Creditcardissuer
CVV	Cvv
ETHEREUMADDRESS	Ethereumaddress
IBAN	Iban
IMEI	Imei
...	and 12 more

Personal Info (11 types)

Entity	Description
AGE	Age
DATEOFBIRTH	Dateofbirth
EYECOLOR	Eyecolor
FIRSTNAME	Firstname
GENDER	Gender
HEIGHT	Height
LASTNAME	Lastname
MIDDLENAME	Middlename
OCCUPATION	Occupation
PREFIX	Prefix
...	and 1 more

Contact Info (2 types)

Entity	Description
EMAIL	Email
PHONE	Phone

Location (9 types)

Entity	Description
BUILDINGNUMBER	Buildingnumber
CITY	City
COUNTY	County
GPSCOORDINATES	Gpscoordinates
ORDINALDIRECTION	Ordinaldirection
SECONDARYADDRESS	Secondaryaddress
STATE	State
STREET	Street
ZIPCODE	Zipcode

Organization (3 types)

Entity	Description
JOBDEPARTMENT	Jobdepartment
JOBTITLE	Jobtitle
ORGANIZATION	Organization

Financial (5 types)

Entity	Description
AMOUNT	Amount
CURRENCY	Currency
CURRENCYCODE	Currencycode
CURRENCYNAME	Currencyname
CURRENCYSYMBOL	Currencysymbol

Temporal (2 types)

Entity	Description
DATE	Date
TIME	Time

Usage

Quick Start

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="OpenMed/OpenMed-PII-German-mClinicalE5-Large-560M-v1", aggregation_strategy="simple")

text = """
Patient Hans Schmidt (geboren am 15.03.1985, SVN: 12 150385 M 234) wurde heute untersucht.
Kontakt: hans.schmidt@email.de, Telefon: 0171 234 5678.
Adresse: Mozartstraße 15, 80336 München.
"""

entities = ner(text)
for entity in entities:
    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")

De-identification Example

def redact_pii(text, entities, placeholder='[REDACTED]'):
    """Replace detected PII with placeholders."""
    # Sort entities by start position (descending) to preserve offsets
    sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
    redacted = text
    for ent in sorted_entities:
        redacted = redacted[:ent['start']] + f"[{ent['entity_group']}]" + redacted[ent['end']:]
    return redacted

# Apply de-identification
redacted_text = redact_pii(text, entities)
print(redacted_text)

Batch Processing

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "OpenMed/OpenMed-PII-German-mClinicalE5-Large-560M-v1"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

texts = [
    "Patient Hans Schmidt (geboren am 15.03.1985, SVN: 12 150385 M 234) wurde heute untersucht.",
    "Kontakt: hans.schmidt@email.de, Telefon: 0171 234 5678.",
]

inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

Training Details

Dataset

• Source: AI4Privacy PII Masking 400k (German subset)
• Format: BIO-tagged token classification
• Labels: 109 total (54 entity types × 2 BIO tags + O)

Training Configuration

• Max Sequence Length: 512 tokens
• Epochs: 3
• Framework: Hugging Face Transformers + Trainer API

Intended Use & Limitations

Intended Use

• De-identification: Automated redaction of PII in German clinical notes, medical records, and documents
• Compliance: Supporting GDPR, and other privacy regulation compliance
• Data Preprocessing: Preparing datasets for research by removing sensitive information
• Audit Support: Identifying PII in document collections

Limitations

Important: This model is intended as an assistive tool, not a replacement for human review.

• False Negatives: Some PII may not be detected; always verify critical applications
• Context Sensitivity: Performance may vary with domain-specific terminology
• Language: Optimized for German text; may not perform well on other languages

Citation

@misc{openmed-pii-2026,
  title = {OpenMed-PII-German-mClinicalE5-Large-560M-v1: German PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/OpenMed/OpenMed-PII-German-mClinicalE5-Large-560M-v1}
}

Links

• Organization: OpenMed