Token Classification
Transformers
Safetensors
PyTorch
Portuguese
xlm-roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
portuguese
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Portuguese-BigMed-Large-278M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Portuguese-BigMed-Large-278M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Portuguese-BigMed-Large-278M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Portuguese-BigMed-Large-278M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Portuguese-BigMed-Large-278M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9335341640421745, | |
| "eval_f1": 0.8837263060564031, | |
| "eval_loss": 0.8926510214805603, | |
| "eval_macro_f1": 0.5308610247508264, | |
| "eval_precision": 0.8814848973945123, | |
| "eval_recall": 0.8859791425260718, | |
| "eval_runtime": 1.4667, | |
| "eval_samples_per_second": 2045.437, | |
| "eval_steps_per_second": 32.045, | |
| "eval_weighted_f1": 0.8750939050593658, | |
| "test_accuracy": 0.9353129182943153, | |
| "test_f1": 0.8854283395803547, | |
| "test_loss": 0.8730665445327759, | |
| "test_macro_f1": 0.5257095294802321, | |
| "test_precision": 0.8849837787733663, | |
| "test_recall": 0.8858733472512178, | |
| "test_runtime": 1.5511, | |
| "test_samples_per_second": 1934.109, | |
| "test_steps_per_second": 30.301, | |
| "test_weighted_f1": 0.87629542134262, | |
| "total_flos": 1465515478351872.0, | |
| "train_loss": 3.5720054931640624, | |
| "train_runtime": 237.1479, | |
| "train_samples_per_second": 303.608, | |
| "train_steps_per_second": 4.744 | |
| } |