Token Classification
Transformers
Safetensors
PyTorch
Spanish
deberta-v2
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
spanish
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Spanish-SuperClinical-Large-434M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Spanish-SuperClinical-Large-434M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Spanish-SuperClinical-Large-434M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Spanish-SuperClinical-Large-434M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Spanish-SuperClinical-Large-434M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_accuracy": 0.9958262528694511, | |
| "test_f1": 0.9491478066499023, | |
| "test_loss": 0.014277148991823196, | |
| "test_macro_f1": 0.9543623695924328, | |
| "test_precision": 0.9515406162464985, | |
| "test_recall": 0.9467670011148273, | |
| "test_runtime": 5.4101, | |
| "test_samples_per_second": 613.48, | |
| "test_steps_per_second": 19.223, | |
| "test_weighted_f1": 0.9489653588530546 | |
| } |