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:
- 3e62a93224e5624a0abf976f6746e61bdce3389556641b67314e6c01913f2f87
- Size of remote file:
- 436 MB
- SHA256:
- 09e2dfeb86430a529c08c1af58f0a51fe2d2951741915da73a9fb8bbed8c6089
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