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
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9964943428021185, | |
| "eval_f1": 0.952116585704372, | |
| "eval_loss": 0.012063526548445225, | |
| "eval_macro_f1": 0.9565422797672655, | |
| "eval_precision": 0.9506651884700665, | |
| "eval_recall": 0.9535724214623297, | |
| "eval_runtime": 5.4391, | |
| "eval_samples_per_second": 610.215, | |
| "eval_steps_per_second": 19.121, | |
| "eval_weighted_f1": 0.9520085638137161 | |
| } |