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StanfordAIMI
/
stanford-deidentifier-v2

Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
bdf-toolbox
Model card Files Files and versions
xet
Community
1

Instructions to use StanfordAIMI/stanford-deidentifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use StanfordAIMI/stanford-deidentifier-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="StanfordAIMI/stanford-deidentifier-v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/stanford-deidentifier-v2")
    model = AutoModelForTokenClassification.from_pretrained("StanfordAIMI/stanford-deidentifier-v2")
  • Notebooks
  • Google Colab
  • Kaggle

Paper: https://arxiv.org/pdf/2511.04079

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Paper for StanfordAIMI/stanford-deidentifier-v2

Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods

Paper • 2511.04079 • Published Nov 6, 2025 • 1
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