Token Classification
Transformers
ONNX
Safetensors
xlm-roberta
ner
on-device
privacy
flowx
openner
cross
de-identification
Instructions to use flowxai/piiguard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/piiguard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/piiguard")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/piiguard") model = AutoModelForTokenClassification.from_pretrained("flowxai/piiguard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f41c02392f3f21113d2840f24bf4119e8e63aec5f93cd2870bb8c746a70948d8
- Size of remote file:
- 5.2 kB
- SHA256:
- 7c93b11a2f6b6f356f5396153df716d1c52d8aac19c01d606817d36bc3e5c2b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.