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-ClinicDischarge-Base-110M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-ClinicDischarge-Base-110M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-ClinicDischarge-Base-110M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-ClinicDischarge-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-ClinicDischarge-Base-110M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9942898431258809, | |
| "eval_f1": 0.9538097791644323, | |
| "eval_loss": 0.022721193730831146, | |
| "eval_precision": 0.955253393223318, | |
| "eval_recall": 0.9523705218068536, | |
| "eval_runtime": 14.8599, | |
| "eval_samples_per_second": 336.476, | |
| "eval_steps_per_second": 5.316 | |
| } |