Token Classification
Transformers
Safetensors
PyTorch
Telugu
bert
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-335M-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-335M-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-335M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-335M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Telugu-ClinicalBGE-Large-335M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9586623316302834, | |
| "eval_f1": 0.8961407491486948, | |
| "eval_loss": 0.17019526660442352, | |
| "eval_macro_f1": 0.8810269706678855, | |
| "eval_precision": 0.9148319814600232, | |
| "eval_recall": 0.8781979977753059, | |
| "eval_runtime": 2.1158, | |
| "eval_samples_per_second": 1046.408, | |
| "eval_steps_per_second": 33.084, | |
| "eval_weighted_f1": 0.8935680232358649, | |
| "test_accuracy": 0.9611529860018222, | |
| "test_f1": 0.9039803036520312, | |
| "test_loss": 0.16051025688648224, | |
| "test_macro_f1": 0.8947686981196402, | |
| "test_precision": 0.922400558269365, | |
| "test_recall": 0.8862813463859461, | |
| "test_runtime": 3.3369, | |
| "test_samples_per_second": 663.495, | |
| "test_steps_per_second": 20.978, | |
| "test_weighted_f1": 0.9017058353576308, | |
| "total_flos": 2612992105512960.0, | |
| "train_loss": 0.3671874357331387, | |
| "train_runtime": 345.2409, | |
| "train_samples_per_second": 153.945, | |
| "train_steps_per_second": 2.407 | |
| } |