Token Classification
Transformers
Safetensors
PyTorch
French
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
french
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-French-ModernMed-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-French-ModernMed-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-French-ModernMed-Base-149M-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification_report.txt from OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1: direct link, hf CLI and curl.
- Browser
- Download file 3.51 kB
-
https://huggingface.co/OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1/resolve/main/classification_report.txt
- Command line
-
hf download hf://OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1/classification_report.txt
-
curl -L -o classification_report.txt https://huggingface.co/OpenMed/OpenMed-PII-French-ModernMed-Base-149M-v1/resolve/main/classification_report.txt
3.51 kB
| Classification Report for French PII Detection | |
| Model: answerdotai/ModernBERT-base | |
| ============================================================ | |
| precision recall f1-score support | |
| ACCOUNTNAME 1.00 1.00 1.00 360 | |
| AGE 0.97 0.98 0.98 389 | |
| AMOUNT 0.96 0.90 0.93 104 | |
| BANKACCOUNT 0.99 1.00 0.99 312 | |
| BIC 0.98 0.98 0.98 98 | |
| BITCOINADDRESS 0.95 0.99 0.97 318 | |
| BUILDINGNUMBER 0.93 0.89 0.91 396 | |
| CITY 0.89 0.89 0.89 329 | |
| COUNTY 0.97 0.98 0.98 382 | |
| CREDITCARD 0.85 0.90 0.88 382 | |
| CREDITCARDISSUER 1.00 1.00 1.00 210 | |
| CURRENCY 0.56 0.75 0.64 210 | |
| CURRENCYCODE 0.94 0.91 0.92 106 | |
| CURRENCYNAME 0.19 0.18 0.19 109 | |
| CURRENCYSYMBOL 0.97 0.99 0.98 355 | |
| CVV 0.90 0.95 0.92 83 | |
| DATE 0.74 0.85 0.79 598 | |
| DATEOFBIRTH 0.70 0.61 0.65 404 | |
| EMAIL 1.00 1.00 1.00 495 | |
| ETHEREUMADDRESS 1.00 1.00 1.00 236 | |
| EYECOLOR 0.99 1.00 0.99 162 | |
| FIRSTNAME 0.95 0.96 0.95 1927 | |
| GENDER 1.00 1.00 1.00 412 | |
| GPSCOORDINATES 1.00 1.00 1.00 300 | |
| HEIGHT 0.99 0.99 0.99 155 | |
| IBAN 0.99 1.00 1.00 273 | |
| IMEI 1.00 1.00 1.00 304 | |
| IPADDRESS 1.00 1.00 1.00 992 | |
| JOBDEPARTMENT 0.98 0.97 0.98 336 | |
| JOBTITLE 0.98 1.00 0.99 329 | |
| LASTNAME 0.95 0.92 0.93 585 | |
| LITECOINADDRESS 0.97 0.84 0.90 110 | |
| MACADDRESS 1.00 1.00 1.00 145 | |
| MASKEDNUMBER 0.87 0.81 0.84 302 | |
| MIDDLENAME 0.92 0.89 0.91 374 | |
| OCCUPATION 1.00 0.99 0.99 382 | |
| ORDINALDIRECTION 1.00 1.00 1.00 185 | |
| ORGANIZATION 0.97 1.00 0.98 322 | |
| PASSWORD 0.99 0.98 0.99 393 | |
| PHONE 0.98 0.99 0.99 341 | |
| PIN 0.88 0.92 0.90 83 | |
| PREFIX 0.97 0.98 0.98 391 | |
| SECONDARYADDRESS 1.00 1.00 1.00 357 | |
| SEX 1.00 1.00 1.00 422 | |
| SSN 0.99 1.00 1.00 331 | |
| STATE 0.95 0.96 0.96 348 | |
| STREET 0.90 0.93 0.92 409 | |
| TIME 0.98 0.99 0.98 348 | |
| URL 1.00 1.00 1.00 364 | |
| USERAGENT 1.00 1.00 1.00 295 | |
| USERNAME 1.00 0.98 0.99 348 | |
| VIN 1.00 0.97 0.99 113 | |
| VRM 0.98 0.99 0.99 125 | |
| ZIPCODE 0.90 0.93 0.92 346 | |
| micro avg 0.94 0.95 0.95 18485 | |
| macro avg 0.94 0.94 0.94 18485 | |
| weighted avg 0.95 0.95 0.95 18485 | |