Token Classification
Transformers
Safetensors
PyTorch
English
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-FastClinical-Small-82M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-FastClinical-Small-82M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-FastClinical-Small-82M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-FastClinical-Small-82M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-FastClinical-Small-82M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9944782542656068, | |
| "eval_f1": 0.9575298969573839, | |
| "eval_loss": 0.02151305042207241, | |
| "eval_precision": 0.9602049530315969, | |
| "eval_recall": 0.954869704469355, | |
| "eval_runtime": 11.4312, | |
| "eval_samples_per_second": 437.399, | |
| "eval_steps_per_second": 6.911, | |
| "test_accuracy": 0.9945807710808351, | |
| "test_f1": 0.9582737491312024, | |
| "test_loss": 0.020685501396656036, | |
| "test_precision": 0.9599993597336492, | |
| "test_recall": 0.9565543310100639, | |
| "test_runtime": 167.8463, | |
| "test_samples_per_second": 268.102, | |
| "test_steps_per_second": 4.194, | |
| "total_flos": 9196425130573824.0, | |
| "train_loss": 0.10089913170127682, | |
| "train_runtime": 528.0645, | |
| "train_samples_per_second": 284.056, | |
| "train_steps_per_second": 8.88 | |
| } |