---
license: cc-by-4.0
language:
  - en
  - it
  - multilingual
library_name: transformers
pipeline_tag: text-classification
base_model: distilbert-base-multilingual-cased
tags:
  - matrix-bios
  - content-safety
  - guardrail
  - moderation
  - trust-and-safety
  - enterprise
datasets:
  - nvidia/Aegis-AI-Content-Safety-Dataset-2.0
---

<p align="center"><b>MATRIX&nbsp;BIOS</b> · <b>Sentinel</b></p>
<p align="center"><i>Fast, multilingual content-safety guardrail.</i></p>

# Matrix-BIOS-Sentinel-0.1

**Developer:** Agent-Matrix · **Version:** 0.1 · **Task:** content-safety
classification · **License:** CC-BY-4.0

Sentinel is the **content-safety guardrail** of the **Matrix BIOS** family: a
small, fast, multilingual classifier that flags unsafe content (safe / unsafe) to
protect AI applications at scale. It is designed to run **on-premise** with low
latency and predictable cost.

## Model overview

- **Architecture:** multilingual encoder classifier
  (base: `distilbert-base-multilingual-cased`).
- **Output:** `safe` / `unsafe` with a calibrated risk score.
- **Optimised for:** real-time guardrailing of model inputs and outputs.

## Intended use

**Primary use cases**
- Content moderation and guardrails for chat, agents, and generation pipelines.
- A fast pre-screen that flags potentially harmful content for review or blocking.

**Out of scope (important)**
- Sentinel classifies **content safety** (harmful content), **not** operational or
  business risk. It will, by design, treat operational actions (e.g. deployments)
  as content-safe. **Operational and policy decisions are made by the governance
  layer, not by this classifier.**
- Decisions with legal or safety consequences require human review.

## How to use

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("ruslanmv/Matrix-BIOS-Sentinel-0.1")
model = AutoModelForSequenceClassification.from_pretrained("ruslanmv/Matrix-BIOS-Sentinel-0.1").eval()
p = torch.softmax(model(**tok("text to screen", return_tensors="pt")).logits, -1)[0]
print("P(unsafe):", float(p[1]))
```

## Governance & responsible use

Sentinel is **advisory**: it produces a recommendation, never a final authority. It
operates inside Matrix OS, where high-risk actions remain gated by policy and human
approval. It is a v0.1 release; evaluate on your own distribution before relying on
it for moderation decisions.

## Citing this work

Matrix BIOS models implement the governed-memory architecture described in our
paper. If you use them in research or production, please cite:

> Magaña Vsevolodovna, R. I. (2026). *Governed Memory: A Bio-Inspired,
> Governance-First Memory Architecture for Continual AI Systems* (1.0). Zenodo.
> https://doi.org/10.5281/zenodo.20615572

```bibtex
@misc{magana2026governedmemory,
  title     = {Governed Memory: A Bio-Inspired, Governance-First Memory
               Architecture for Continual AI Systems},
  author    = {Maga{\~n}a Vsevolodovna, Ruslan Idelfonso},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0},
  doi       = {10.5281/zenodo.20615572},
  url       = {https://doi.org/10.5281/zenodo.20615572}
}
```

The concept DOI [10.5281/zenodo.20615571](https://doi.org/10.5281/zenodo.20615571)
always resolves to the latest version.

## License & attribution

Released under **CC-BY-4.0**. Base model `distilbert-base-multilingual-cased`
(Apache-2.0). Safety training data: NVIDIA Aegis AI Content Safety Dataset 2.0
(CC-BY-4.0). © Agent-Matrix.
Contact: **contact@ruslanmv.com** · https://ruslanmv.com