Token Classification
Transformers
Safetensors
PyTorch
German
deberta-v2
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use kranushealth/OpenMed-PII-German-SuperClinical-Large-434M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kranushealth/OpenMed-PII-German-SuperClinical-Large-434M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kranushealth/OpenMed-PII-German-SuperClinical-Large-434M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kranushealth/OpenMed-PII-German-SuperClinical-Large-434M-v1") model = AutoModelForTokenClassification.from_pretrained("kranushealth/OpenMed-PII-German-SuperClinical-Large-434M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_accuracy": 0.996073449590022, | |
| "test_f1": 0.9760849753307564, | |
| "test_loss": 0.01122661679983139, | |
| "test_macro_f1": 0.9688192429383299, | |
| "test_precision": 0.9744008031120592, | |
| "test_recall": 0.9777749795378706, | |
| "test_runtime": 9.1474, | |
| "test_samples_per_second": 577.213, | |
| "test_steps_per_second": 18.038, | |
| "test_weighted_f1": 0.9757486920797904 | |
| } |