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
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9724781886006235, | |
| "eval_f1": 0.8035084433623623, | |
| "eval_loss": 0.4351467788219452, | |
| "eval_macro_f1": 0.7960797613930578, | |
| "eval_precision": 0.8002860162904931, | |
| "eval_recall": 0.8067569261627178, | |
| "eval_runtime": 10.4537, | |
| "eval_samples_per_second": 505.753, | |
| "eval_steps_per_second": 15.88, | |
| "eval_weighted_f1": 0.801985879573623, | |
| "test_accuracy": 0.973938484043577, | |
| "test_f1": 0.8131744040150565, | |
| "test_loss": 0.4128708243370056, | |
| "test_macro_f1": 0.80338573345591, | |
| "test_precision": 0.8102769269237982, | |
| "test_recall": 0.8160926777057231, | |
| "test_runtime": 9.7214, | |
| "test_samples_per_second": 543.134, | |
| "test_steps_per_second": 16.973, | |
| "test_weighted_f1": 0.8113269030094872, | |
| "total_flos": 2.5805103021686784e+16, | |
| "train_loss": 3.3645925997966355, | |
| "train_runtime": 1546.1519, | |
| "train_samples_per_second": 81.978, | |
| "train_steps_per_second": 1.283 | |
| } |