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
File size: 404 Bytes
12eaebd | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"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
} |