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
- Xet hash:
- 02f3c9cc751b1023fdd25ec9294e2e9d3236525d8ef3a8b04e54ad87568504c2
- Size of remote file:
- 1.74 GB
- SHA256:
- 5762cc5eaabf16f26851e41aaec4301c07ff551343012bfbb11c756598d3ab94
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