Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
Instructions to use StanfordAIMI/stanford-deidentifier-only-i2b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/stanford-deidentifier-only-i2b2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="StanfordAIMI/stanford-deidentifier-only-i2b2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/stanford-deidentifier-only-i2b2") model = AutoModel.from_pretrained("StanfordAIMI/stanford-deidentifier-only-i2b2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79aed2490ac42af9052ab5c09857fb7160895736f7082398f83a4017993e481c
- Size of remote file:
- 438 MB
- SHA256:
- 2111ac20d133fefa3b324f4ecb544916c4d2b2ecefa763392e8f3ea5d0b72ca4
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