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-GTEMed-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-GTEMed-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-GTEMed-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-GTEMed-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-GTEMed-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9948914736484923, | |
| "eval_f1": 0.9574428406251165, | |
| "eval_loss": 0.020075542852282524, | |
| "eval_precision": 0.9665152237756972, | |
| "eval_recall": 0.9485391929841849, | |
| "eval_runtime": 17.9225, | |
| "eval_samples_per_second": 278.98, | |
| "eval_steps_per_second": 4.408 | |
| } |