Token Classification
Transformers
Safetensors
PyTorch
German
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-GTEMed-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-GTEMed-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-GTEMed-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1: direct link, hf CLI and curl.
- Browser
- Download file 206 Bytes
-
https://huggingface.co/OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1/resolve/main/train_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1/train_results.json
-
curl -L -o train_results.json https://huggingface.co/OpenMed/OpenMed-PII-German-GTEMed-Base-149M-v1/resolve/main/train_results.json
206 Bytes
| { | |
| "epoch": 3.0, | |
| "total_flos": 6792784958717952.0, | |
| "train_loss": 0.10604900979839427, | |
| "train_runtime": 368.4138, | |
| "train_samples_per_second": 344.043, | |
| "train_steps_per_second": 5.383 | |
| } |