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 eval_results.json from OpenMed/OpenMed-PII-ModernMed-Large-395M-v1: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/resolve/main/eval_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/OpenMed/OpenMed-PII-ModernMed-Large-395M-v1/resolve/main/eval_results.json
321 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9952940956725396, | |
| "eval_f1": 0.9627136262947518, | |
| "eval_loss": 0.018345490097999573, | |
| "eval_precision": 0.9672742645413175, | |
| "eval_recall": 0.9581957924816475, | |
| "eval_runtime": 32.6558, | |
| "eval_samples_per_second": 153.112, | |
| "eval_steps_per_second": 2.419 | |
| } |