Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
bdf-toolbox
Instructions to use StanfordAIMI/stanford-deidentifier-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/stanford-deidentifier-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="StanfordAIMI/stanford-deidentifier-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/stanford-deidentifier-base") model = AutoModel.from_pretrained("StanfordAIMI/stanford-deidentifier-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from StanfordAIMI/stanford-deidentifier-base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/StanfordAIMI/stanford-deidentifier-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StanfordAIMI/stanford-deidentifier-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StanfordAIMI/stanford-deidentifier-base/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c72477011f4da88227589472e480ab6006907b2578b084f7fab47eaca830257b
- Size of remote file:
- 438 MB
- SHA256:
- fa49ef069171e479f546ce2ee5ed599aa585d1d33bc7a8f54400ac57d9cd2716
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