Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
bdf-toolbox
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6317ccca02aabcc358828f0568cd86b0e7aee1e676dd5a4094b001823c42531c
- Size of remote file:
- 3.77 kB
- SHA256:
- 217d7d82c4d61d6e4fd12679495070688ee1a8b75740d20308e0cd61d6bf0b95
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