Token Classification
Transformers
Safetensors
PyTorch
Spanish
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
spanish
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Spanish-SuperMedical-Large-355M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Spanish-SuperMedical-Large-355M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Spanish-SuperMedical-Large-355M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Spanish-SuperMedical-Large-355M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Spanish-SuperMedical-Large-355M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9950349061145883, | |
| "eval_f1": 0.939031180400891, | |
| "eval_loss": 0.016104647889733315, | |
| "eval_macro_f1": 0.942677973983192, | |
| "eval_precision": 0.9403401170894898, | |
| "eval_recall": 0.9377258826800111, | |
| "eval_runtime": 3.424, | |
| "eval_samples_per_second": 969.342, | |
| "eval_steps_per_second": 30.374, | |
| "eval_weighted_f1": 0.9383653116545607, | |
| "test_accuracy": 0.994738097367558, | |
| "test_f1": 0.9346186085498742, | |
| "test_loss": 0.01693236082792282, | |
| "test_macro_f1": 0.9403247420280537, | |
| "test_precision": 0.9369747899159664, | |
| "test_recall": 0.9322742474916388, | |
| "test_runtime": 3.3433, | |
| "test_samples_per_second": 992.733, | |
| "test_steps_per_second": 31.107, | |
| "test_weighted_f1": 0.9336543539734077, | |
| "total_flos": 9394602335272960.0, | |
| "train_loss": 0.10945054007821294, | |
| "train_runtime": 511.8493, | |
| "train_samples_per_second": 155.659, | |
| "train_steps_per_second": 2.432 | |
| } |