Token Classification
Transformers
Safetensors
PyTorch
German
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-BioClinicalModern-Base-149M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-German-BioClinicalModern-Base-149M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-German-BioClinicalModern-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-BioClinicalModern-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-BioClinicalModern-Base-149M-v1") - Notebooks
- Google Colab
- Kaggle
Upload German PII detection model OpenMed-PII-German-BioClinicalModern-Base-149M-v1
43de4f2 verified | Classification Report for German PII Detection | |
| Model: thomas-sounack/BioClinical-ModernBERT-base | |
| ============================================================ | |
| precision recall f1-score support | |
| ACCOUNTNAME 1.00 1.00 1.00 286 | |
| AGE 0.97 0.96 0.97 363 | |
| AMOUNT 0.98 0.96 0.97 127 | |
| BANKACCOUNT 0.99 1.00 1.00 303 | |
| BIC 0.95 0.99 0.97 76 | |
| BITCOINADDRESS 0.94 0.99 0.96 295 | |
| BUILDINGNUMBER 0.96 0.91 0.93 363 | |
| CITY 0.91 0.85 0.88 303 | |
| COUNTY 0.97 0.98 0.97 335 | |
| CREDITCARD 0.84 0.85 0.84 294 | |
| CREDITCARDISSUER 0.99 1.00 1.00 166 | |
| CURRENCY 0.65 0.77 0.70 213 | |
| CURRENCYCODE 0.95 0.92 0.93 75 | |
| CURRENCYNAME 0.25 0.24 0.25 78 | |
| CURRENCYSYMBOL 0.96 0.97 0.96 317 | |
| CVV 0.97 0.97 0.97 86 | |
| DATE 0.73 0.83 0.78 453 | |
| DATEOFBIRTH 0.70 0.66 0.67 348 | |
| EMAIL 1.00 1.00 1.00 449 | |
| ETHEREUMADDRESS 1.00 1.00 1.00 190 | |
| EYECOLOR 0.96 0.97 0.97 117 | |
| FIRSTNAME 0.95 0.96 0.95 1701 | |
| GENDER 0.99 0.99 0.99 312 | |
| GPSCOORDINATES 1.00 1.00 1.00 217 | |
| HEIGHT 0.99 0.99 0.99 110 | |
| IBAN 0.99 1.00 0.99 265 | |
| IMEI 1.00 1.00 1.00 249 | |
| IPADDRESS 1.00 1.00 1.00 779 | |
| JOBDEPARTMENT 0.99 0.97 0.98 330 | |
| JOBTITLE 0.99 1.00 0.99 333 | |
| LASTNAME 0.93 0.93 0.93 509 | |
| LITECOINADDRESS 0.90 0.73 0.81 75 | |
| MACADDRESS 1.00 1.00 1.00 123 | |
| MASKEDNUMBER 0.81 0.80 0.80 242 | |
| MIDDLENAME 0.92 0.88 0.90 330 | |
| OCCUPATION 0.99 0.99 0.99 344 | |
| ORDINALDIRECTION 1.00 1.00 1.00 141 | |
| ORGANIZATION 0.96 1.00 0.98 292 | |
| PASSWORD 1.00 0.96 0.98 303 | |
| PHONE 0.99 0.99 0.99 300 | |
| PIN 0.96 0.96 0.96 79 | |
| PREFIX 0.95 0.97 0.96 343 | |
| SECONDARYADDRESS 1.00 1.00 1.00 327 | |
| SEX 1.00 1.00 1.00 328 | |
| SSN 1.00 1.00 1.00 287 | |
| STATE 0.95 0.95 0.95 334 | |
| STREET 0.88 0.93 0.91 350 | |
| TIME 0.96 0.98 0.97 306 | |
| URL 1.00 1.00 1.00 288 | |
| USERAGENT 1.00 1.00 1.00 267 | |
| USERNAME 0.98 0.96 0.97 294 | |
| VIN 0.99 0.97 0.98 94 | |
| VRM 0.99 0.99 0.99 97 | |
| ZIPCODE 0.91 0.97 0.94 297 | |
| micro avg 0.94 0.95 0.95 15883 | |
| macro avg 0.94 0.94 0.94 15883 | |
| weighted avg 0.94 0.95 0.95 15883 | |