Instructions to use TensorGreed/SensiGuard-PII with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TensorGreed/SensiGuard-PII with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TensorGreed/SensiGuard-PII")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TensorGreed/SensiGuard-PII") model = AutoModelForTokenClassification.from_pretrained("TensorGreed/SensiGuard-PII", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from TensorGreed/SensiGuard-PII: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/TensorGreed/SensiGuard-PII/resolve/main/training_args.bin
- Command line
-
hf download hf://TensorGreed/SensiGuard-PII/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/TensorGreed/SensiGuard-PII/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 92c999a1aa35228cc30af424e909a4d46d1728869b564c860dff05729a2d63f5
- Size of remote file:
- 5.43 kB
- SHA256:
- 610df77b20e51e77e8effe6f097777f42aaf4d1caaf4b7f25e03492a13e9bb64
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