Token Classification
Transformers
Safetensors
privacy_filter
privacy
pii
secrets
code-security
matex
Instructions to use enosislabs/matex-privacy-sentinel-v0.15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enosislabs/matex-privacy-sentinel-v0.15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="enosislabs/matex-privacy-sentinel-v0.15")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("enosislabs/matex-privacy-sentinel-v0.15", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
base_model: openai/privacy-filter
license: apache-2.0
tags:
- token-classification
- privacy
- pii
- secrets
- code-security
- matex
datasets:
- enosislabs/matex-privacy-sentinel-dataset
MaTE X Privacy Sentinel v0.1
Fine-tuned checkpoint based on OpenAI Privacy Filter for local privacy/security redaction in MaTE X.
Dataset
enosislabs/matex-privacy-sentinel-dataset
Usage
opf --checkpoint . "DATABASE_URL=postgres://demo_user:demo_pass@db.local/matex"
Limitation
This is a privacy/security aid, not a compliance guarantee. Run your own canary evaluation before production.