Token Classification
Transformers
Safetensors
PyTorch
English
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-SuperMedical-Large-355M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ff0c3decf4b16d8a5bd51485c9fbb1224270f6eb2c30f4f464a09226bbce397
- Size of remote file:
- 1.42 GB
- SHA256:
- a59141877bfeba5bc4602e15c077a1bc29f776681704bd8a857f4c79621f1572
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