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:
- 327e34a22fe80fa28f2d14dd8943065c27dda1b485803d05e0cdfa201dbca672
- Size of remote file:
- 278 MB
- SHA256:
- 286c628349c0145fdfbfc773cd44a6e22680abb42b00730d6ec78d366aac610b
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