Instructions to use KindLab/distilbert-deid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KindLab/distilbert-deid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KindLab/distilbert-deid")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KindLab/distilbert-deid") model = AutoModelForTokenClassification.from_pretrained("KindLab/distilbert-deid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KindLab/distilbert-deid: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/KindLab/distilbert-deid/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KindLab/distilbert-deid/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KindLab/distilbert-deid/resolve/main/pytorch_model.bin
261 MB
- Xet hash:
- bffcbc34a24f467818c21bdc23592ff5e60a218a4beccbcf50ea0ebe68034cf1
- Size of remote file:
- 261 MB
- SHA256:
- b7f0b927c31f27672e6709f64add0cd33818021f3a2162dfa84af181528d788e
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