Token Classification
Transformers
Safetensors
PyTorch
English
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-ClinicalE5-Base-109M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-ClinicalE5-Base-109M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-ClinicalE5-Base-109M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-ClinicalE5-Base-109M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-ClinicalE5-Base-109M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.994285048763938, | |
| "eval_f1": 0.9540518164994642, | |
| "eval_loss": 0.023267928510904312, | |
| "eval_precision": 0.9574556473290651, | |
| "eval_recall": 0.9506721017130101, | |
| "eval_runtime": 14.8896, | |
| "eval_samples_per_second": 335.804, | |
| "eval_steps_per_second": 5.306 | |
| } |