Instructions to use KindLab/bert-deid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KindLab/bert-deid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KindLab/bert-deid")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KindLab/bert-deid") model = AutoModelForTokenClassification.from_pretrained("KindLab/bert-deid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KindLab/bert-deid: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/KindLab/bert-deid/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KindLab/bert-deid/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KindLab/bert-deid/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 4fcb2eaf38b463b1c27967740b6249bd04d4f9d85fd1b12e3af05a9aaddde0cc
- Size of remote file:
- 431 MB
- SHA256:
- e08f12a9f9663b83daa139ccb3aa5b720b8d17d97b46702aaede93ffab202014
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