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
| { | |
| "epoch": 3.0, | |
| "total_flos": 6.527024553659597e+16, | |
| "train_loss": 0.06644338119586767, | |
| "train_runtime": 2340.4005, | |
| "train_samples_per_second": 64.092, | |
| "train_steps_per_second": 2.004 | |
| } |