Token Classification
Transformers
Safetensors
PyTorch
Portuguese
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
portuguese
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Portuguese PII detection model OpenMed-PII-Portuguese-ClinicalBGE-Large-335M-v1
0fe946b verified | { | |
| "test_accuracy": 0.9342202311033408, | |
| "test_f1": 0.884407355895534, | |
| "test_loss": 0.8489246368408203, | |
| "test_macro_f1": 0.5654214345154047, | |
| "test_precision": 0.8837927573160373, | |
| "test_recall": 0.8850228098662337, | |
| "test_runtime": 3.1526, | |
| "test_samples_per_second": 951.582, | |
| "test_steps_per_second": 29.816, | |
| "test_weighted_f1": 0.8769077124176684 | |
| } |