Token Classification
Transformers
Safetensors
PyTorch
Dutch
distilbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
dutch
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Dutch-mLiteClinical-Base-135M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Dutch-mLiteClinical-Base-135M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Dutch-mLiteClinical-Base-135M-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Dutch-mLiteClinical-Base-135M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Dutch-mLiteClinical-Base-135M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,227 Bytes
3f02ca0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | Classification Report for Dutch PII Detection
Model: distilbert/distilbert-base-multilingual-cased
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precision recall f1-score support
BANKACCOUNT 0.61 0.35 0.44 136
BUILDINGNUMBER 0.85 0.79 0.82 111
CITY 0.90 0.94 0.92 334
CREDITCARD 0.52 0.67 0.59 86
DATEOFBIRTH 0.87 0.85 0.86 138
EMAIL 0.99 1.00 1.00 347
FIRSTNAME 0.79 0.88 0.83 460
LASTNAME 0.78 0.70 0.74 354
MASKEDNUMBER 0.95 0.94 0.94 77
PASSWORD 0.98 0.98 0.98 94
PHONE 0.99 0.99 0.99 230
SSN 0.90 1.00 0.95 338
STREET 0.82 0.79 0.80 123
USERNAME 0.98 0.96 0.97 367
ZIPCODE 0.91 0.95 0.93 193
micro avg 0.88 0.88 0.88 3388
macro avg 0.86 0.85 0.85 3388
weighted avg 0.88 0.88 0.88 3388
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