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
| 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 | |
| ```bash | |
| 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. | |