Token Classification
Transformers
Safetensors
PyTorch
French
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
french
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-French-BioClinicalModern-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-French-BioClinicalModern-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-French-BioClinicalModern-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-French-BioClinicalModern-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-French-BioClinicalModern-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload French PII detection model OpenMed-PII-French-BioClinicalModern-Base-149M-v1
561a0eb verified | { | |
| "test_accuracy": 0.9932545197625126, | |
| "test_f1": 0.9469995147463202, | |
| "test_loss": 0.018558146432042122, | |
| "test_macro_f1": 0.9389909588973192, | |
| "test_precision": 0.9438443763770219, | |
| "test_recall": 0.9501758182309981, | |
| "test_runtime": 5.2344, | |
| "test_samples_per_second": 1178.936, | |
| "test_steps_per_second": 18.531, | |
| "test_weighted_f1": 0.9476817531262937 | |
| } |