Token Classification
Transformers
ONNX
xlm-roberta
pii
privacy
redaction
accessibility-tree
ocr
computer-use
agentic
screen-capture
screenpipe
Instructions to use screenpipe/pii-redactor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use screenpipe/pii-redactor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="screenpipe/pii-redactor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("screenpipe/pii-redactor") model = AutoModelForTokenClassification.from_pretrained("screenpipe/pii-redactor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce1f7808ee335a5594bceaad1ed1c579a96bb2d5c2b1aa4f5c3958518e808819
- Size of remote file:
- 149 MB
- SHA256:
- a966fe75b8b7b9042b6c4a9a5d3878ca3e4a00fdbae26e8fbc9be4f4bebf5a61
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