Token Classification
Transformers
Safetensors
PyTorch
German
distilbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-mLiteClinical-Base-135M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-German-mLiteClinical-Base-135M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-German-mLiteClinical-Base-135M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-mLiteClinical-Base-135M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-mLiteClinical-Base-135M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9923401582523305, | |
| "eval_f1": 0.9532452901045253, | |
| "eval_loss": 0.023432230576872826, | |
| "eval_macro_f1": 0.9362286962029015, | |
| "eval_precision": 0.951875, | |
| "eval_recall": 0.9546195311520622, | |
| "eval_runtime": 2.5782, | |
| "eval_samples_per_second": 2050.635, | |
| "eval_steps_per_second": 16.29, | |
| "eval_weighted_f1": 0.9499855832894755 | |
| } |