Token Classification
Transformers
Safetensors
PyTorch
Telugu
xlm-roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
telugu
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-568M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-568M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-568M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-568M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-568M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_accuracy": 0.9831442060796819, | |
| "test_f1": 0.9485047606275983, | |
| "test_loss": 0.05164899677038193, | |
| "test_macro_f1": 0.9600213274150518, | |
| "test_precision": 0.9485047606275983, | |
| "test_recall": 0.9485047606275983, | |
| "test_runtime": 1.9862, | |
| "test_samples_per_second": 1114.695, | |
| "test_steps_per_second": 17.622, | |
| "test_weighted_f1": 0.9479569709431587 | |
| } |