Token Classification
Transformers
Safetensors
privacy_filter
privacy
pii
secrets
code-security
matex
Instructions to use enosislabs/aether-privacy-v0.17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enosislabs/aether-privacy-v0.17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="enosislabs/aether-privacy-v0.17")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("enosislabs/aether-privacy-v0.17", 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/aether-privacy-dataset
MaTE X Privacy Sentinel v0.1
Fine-tuned checkpoint based on OpenAI Privacy Filter for local privacy/security redaction in MaTE X.
Privacy Filter commit: f7f00ca7fb869683eb732c010299d901457f19c3
Validation span F1: 0.995583
Test span F1: 0.989417
Dataset
enosislabs/aether-privacy-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.