Token Classification
Transformers
Safetensors
PyTorch
English
longformer
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-ClinicalLongformer-Base-149M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-ClinicalLongformer-Base-149M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-ClinicalLongformer-Base-149M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-ClinicalLongformer-Base-149M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-ClinicalLongformer-Base-149M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 725ad7a6311cf705731bc797e7c1ae1b35619ec3722f035f6f1fcafba6c2b2b7
- Size of remote file:
- 593 MB
- SHA256:
- 65d1523f25381e6fc4cc3233770a37772eb2e1ecf6025494f93853ff04aca9c7
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