Token Classification
Transformers
Safetensors
PyTorch
German
qwen3
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
text-generation-inference
Instructions to use OpenMed/OpenMed-PII-German-QwenMed-XLarge-600M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-German-QwenMed-XLarge-600M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-German-QwenMed-XLarge-600M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-QwenMed-XLarge-600M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-QwenMed-XLarge-600M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f9d2f860b03b86c3b84bc0ba88a3b92d34370314bd53a5e1efca703972a428d
- Size of remote file:
- 11.4 MB
- SHA256:
- 4689bbafe5096d0a16cf2849ede8c67a98d4925bde8eba491f4a6839b1954fc6
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