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 spm.model from TensorGreed/SensiGuard-PII: direct link, hf CLI and curl.
- Browser
- Download file 2.46 MB
-
https://huggingface.co/TensorGreed/SensiGuard-PII/resolve/main/spm.model
- Command line
-
hf download hf://TensorGreed/SensiGuard-PII/spm.model
-
curl -L -o spm.model https://huggingface.co/TensorGreed/SensiGuard-PII/resolve/main/spm.model
2.46 MB
- Xet hash:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.