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-ModernMed-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-ModernMed-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-ModernMed-Large-395M-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-ModernMed-Large-395M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-ModernMed-Large-395M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-PII-ModernMed-Large-395M-v1: direct link, hf CLI and curl.
- Browser
- Download file 304 Bytes
-
https://huggingface.co/OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/resolve/main/test_results.json
304 Bytes
| { | |
| "test_accuracy": 0.9951424824909889, | |
| "test_f1": 0.9613336345301398, | |
| "test_loss": 0.018015222623944283, | |
| "test_precision": 0.9645340788795093, | |
| "test_recall": 0.9581543588872441, | |
| "test_runtime": 328.2736, | |
| "test_samples_per_second": 137.081, | |
| "test_steps_per_second": 2.145 | |
| } |