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
chore: remove retired OpenAI-Privacy-Filter model + its provenance trail (served model is screenpipe's own); rewrite NOTICE
932040a verified | 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. | |