Token Classification
Transformers
Safetensors
PyTorch
Hindi
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hindi
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Hindi-BioClinicalBERT-Base-110M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Hindi-BioClinicalBERT-Base-110M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Hindi-BioClinicalBERT-Base-110M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Hindi-BioClinicalBERT-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Hindi-BioClinicalBERT-Base-110M-v1") - Notebooks
- Google Colab
- Kaggle
File size: 975 Bytes
95a0350 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"epoch": 3.0,
"eval_accuracy": 0.9850629112316749,
"eval_f1": 0.9225602528644805,
"eval_loss": 0.04921209067106247,
"eval_macro_f1": 0.9385026095071556,
"eval_precision": 0.920378399684667,
"eval_recall": 0.9247524752475248,
"eval_runtime": 1.9359,
"eval_samples_per_second": 1395.709,
"eval_steps_per_second": 22.212,
"eval_weighted_f1": 0.9205632087034019,
"test_accuracy": 0.9858790087797722,
"test_f1": 0.9280410067687455,
"test_loss": 0.05012573301792145,
"test_macro_f1": 0.940815574304646,
"test_precision": 0.9254259501965924,
"test_recall": 0.9306708844075392,
"test_runtime": 1.9478,
"test_samples_per_second": 1387.21,
"test_steps_per_second": 22.076,
"test_weighted_f1": 0.9267615644813842,
"total_flos": 1680804629446656.0,
"train_loss": 0.23875660909233243,
"train_runtime": 122.3794,
"train_samples_per_second": 529.918,
"train_steps_per_second": 8.286
} |