Token Classification
Transformers
Safetensors
PyTorch
English
distilbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-mLiteClinical-Base-135M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.994227345682003, | |
| "eval_f1": 0.9554599831924465, | |
| "eval_loss": 0.023433899506926537, | |
| "eval_precision": 0.9623341382658269, | |
| "eval_recall": 0.9486833386506982, | |
| "eval_runtime": 12.7752, | |
| "eval_samples_per_second": 391.382, | |
| "eval_steps_per_second": 6.184, | |
| "test_accuracy": 0.9941098956458422, | |
| "test_f1": 0.9538954711743647, | |
| "test_loss": 0.022977333515882492, | |
| "test_precision": 0.9597516568448018, | |
| "test_recall": 0.9481103182961164, | |
| "test_runtime": 171.9143, | |
| "test_samples_per_second": 261.758, | |
| "test_steps_per_second": 4.095, | |
| "total_flos": 1.0002172609668096e+16, | |
| "train_loss": 0.09353462532146777, | |
| "train_runtime": 683.8238, | |
| "train_samples_per_second": 219.355, | |
| "train_steps_per_second": 6.857 | |
| } |