Token Classification
Transformers
Safetensors
PyTorch
English
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-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-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-SuperMedical-Large-355M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_accuracy": 0.9951405995534098, | |
| "test_f1": 0.9643429593852675, | |
| "test_loss": 0.017982345074415207, | |
| "test_precision": 0.965927213957681, | |
| "test_recall": 0.9627638930977103, | |
| "test_runtime": 290.3578, | |
| "test_samples_per_second": 154.981, | |
| "test_steps_per_second": 2.425 | |
| } |