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
File size: 386 Bytes
c29cec6 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"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
} |