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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 432 Bytes
1503442 932040a 1503442 932040a 1503442 932040a | 1 2 3 4 5 6 7 8 9 10 11 | screenpipe-pii-redactor
Copyright 2026 screenpipe (https://screenpi.pe)
screenpipe's own PII token-classification model, trained in-house for
redacting PII in screen telemetry (accessibility trees, OCR'd screen
text, computer-use traces).
Distributed under CC BY-NC 4.0 (non-commercial) — see LICENSE.
For commercial licensing (production deployment, SaaS / API embedding,
redistribution, custom fine-tunes): louis@screenpi.pe.
|