Token Classification
Transformers
Safetensors
PyTorch
English
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-BioClinicalModern-Base-149M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-BioClinicalModern-Base-149M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-BioClinicalModern-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-BioClinicalModern-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-BioClinicalModern-Base-149M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9946434667831642, | |
| "eval_f1": 0.956490363679345, | |
| "eval_loss": 0.020970426499843597, | |
| "eval_precision": 0.9662887453178813, | |
| "eval_recall": 0.9468887027639553, | |
| "eval_runtime": 17.8281, | |
| "eval_samples_per_second": 280.455, | |
| "eval_steps_per_second": 4.431, | |
| "test_accuracy": 0.9945323048189167, | |
| "test_f1": 0.9549491203030331, | |
| "test_loss": 0.020490840077400208, | |
| "test_precision": 0.9638099893986916, | |
| "test_recall": 0.9462496932829644, | |
| "test_runtime": 210.6881, | |
| "test_samples_per_second": 213.586, | |
| "test_steps_per_second": 3.341, | |
| "total_flos": 2.6007657400541184e+16, | |
| "train_loss": 0.0728830616022636, | |
| "train_runtime": 1230.3674, | |
| "train_samples_per_second": 121.915, | |
| "train_steps_per_second": 3.811 | |
| } |