Token Classification
Transformers
Safetensors
PyTorch
German
longformer
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-ClinicalLongformer-Base-149M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-German-ClinicalLongformer-Base-149M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-German-ClinicalLongformer-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-ClinicalLongformer-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-ClinicalLongformer-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload German PII detection model OpenMed-PII-German-ClinicalLongformer-Base-149M-v1
7f4248d verified | { | |
| "epoch": 3.0, | |
| "total_flos": 6598643039600640.0, | |
| "train_loss": 0.18558713852367315, | |
| "train_runtime": 1301.2333, | |
| "train_samples_per_second": 97.408, | |
| "train_steps_per_second": 1.524 | |
| } |