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, | |
| "eval_accuracy": 0.9951769530656676, | |
| "eval_f1": 0.9635658066007323, | |
| "eval_loss": 0.0189244132488966, | |
| "eval_precision": 0.9662104467052136, | |
| "eval_recall": 0.9609356044062697, | |
| "eval_runtime": 28.242, | |
| "eval_samples_per_second": 177.041, | |
| "eval_steps_per_second": 2.797, | |
| "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, | |
| "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 | |
| } |