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
File size: 976 Bytes
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"eval_f1": 0.952116585704372,
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"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,
"test_accuracy": 0.9958262528694511,
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"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,
"total_flos": 8325797257412608.0,
"train_loss": 0.08537046103831755,
"train_runtime": 1133.3513,
"train_samples_per_second": 70.299,
"train_steps_per_second": 2.197
} |