Token Classification
Transformers
Safetensors
PyTorch
English
xlm-roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-ClinicalBGE-Large-568M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-ClinicalBGE-Large-568M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-ClinicalBGE-Large-568M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-ClinicalBGE-Large-568M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-ClinicalBGE-Large-568M-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d3a9ab194466b6e3502faf1baa7aa66ed010b9dc04a580da3c830849b64c149
- Size of remote file:
- 17.1 MB
- SHA256:
- 33cd99e33ce09bdd8a6136fddfe90a1c47f85bafedf7309d0eecc19012d43586
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