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-BiomedBERTFull-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-Telugu-BiomedBERTFull-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-Telugu-BiomedBERTFull-Base-110M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Telugu-BiomedBERTFull-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Telugu-BiomedBERTFull-Base-110M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e165ff72b11d470bd53b4801411f43e663b600a57c60460476e032208fec19d
- Size of remote file:
- 436 MB
- SHA256:
- c7e25f701593590110535a653c1d38d92e3a7bb0060c737bff2fb088b4b5487e
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