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-NomicMed-Large-395M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-NomicMed-Large-395M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-NomicMed-Large-395M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-NomicMed-Large-395M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-NomicMed-Large-395M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9952141938791076, | |
| "eval_f1": 0.9632120351689955, | |
| "eval_loss": 0.018534738570451736, | |
| "eval_precision": 0.9740076566592787, | |
| "eval_recall": 0.9526531014435631, | |
| "eval_runtime": 32.2326, | |
| "eval_samples_per_second": 155.123, | |
| "eval_steps_per_second": 2.451, | |
| "test_accuracy": 0.9951492304727418, | |
| "test_f1": 0.962957719059165, | |
| "test_loss": 0.01781981810927391, | |
| "test_precision": 0.9724727504701988, | |
| "test_recall": 0.9536270806200673, | |
| "test_runtime": 324.5777, | |
| "test_samples_per_second": 138.642, | |
| "test_steps_per_second": 2.169, | |
| "total_flos": 8.068265772611174e+16, | |
| "train_loss": 0.04793148326528024, | |
| "train_runtime": 2783.9136, | |
| "train_samples_per_second": 53.881, | |
| "train_steps_per_second": 1.684 | |
| } |