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
File size: 975 Bytes
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"eval_f1": 0.8961407491486948,
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"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
} |