Token Classification
Transformers
Safetensors
PyTorch
Hindi
modernbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hindi
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Hindi-ModernMed-Base-149M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9858685847769272, | |
| "eval_f1": 0.9243353783231083, | |
| "eval_loss": 0.0465334914624691, | |
| "eval_macro_f1": 0.9374901002738825, | |
| "eval_precision": 0.922813487881981, | |
| "eval_recall": 0.9258622968151182, | |
| "eval_runtime": 2.6281, | |
| "eval_samples_per_second": 1028.114, | |
| "eval_steps_per_second": 16.362, | |
| "eval_weighted_f1": 0.9229231978984003 | |
| } |