MODEL PROFILE

rampart

nationaldesignstudio · rampart

`rampart` is a 14.7 MB ONNX token-classification model that detects personally identifiable information (PII) in text before it leaves the user's device. It is the on-device half of **Rampart**, a defense-in-depth client-side redaction system released by National Design Studio. The shipped artifact runs alongside a deterministic recognizer layer that handles structured identifiers; together they form the complete system. This card documents the released artifact only. Alternative configurations explored during model selection (an ELECTRA-small base, the prefilter-off training variant, leaner data mixes, and smaller corpus slices) are discussed in the project whitepaper for context but are not published. The model is designed for **client-side redaction of user-typed text in AI assistants and intake flows** — replacing identifying values with stable placeholders before any data is transmitted to a model provider, a server, or a logging system.

Text analysislicense:cc-by-4.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 19:21:06
OVERVIEW

Model overview

`rampart` is a 14.7 MB ONNX token-classification model that detects personally identifiable information (PII) in text before it leaves the user's device. It is the on-device half of **Rampart**, a defense-in-depth client-side redaction system released by National Design Studio. The shipped artifact runs alongside a deterministic recognizer layer that handles structured identifiers; together they form the complete system. This card documents the released artifact only. Alternative configurations explored during model selection (an ELECTRA-small base, the prefilter-off training variant, leaner data mixes, and smaller corpus slices) are discussed in the project whitepaper for context but are not published. The model is designed for **client-side redaction of user-typed text in AI assistants and intake flows** — replacing identifying values with stable placeholders before any data is transmitted to a model provider, a server, or a logging system.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。