Token Classification
Transformers
Safetensors
PyTorch
German
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d80c53f25a5184758d7607d0e5fabb5660efbbb2d350ec025e76b69c930227e5
- Size of remote file:
- 436 MB
- SHA256:
- e159f56b74f17b4d171ed0b632e74dbe0791bc52df4db54baac5005c0c917b2d
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