| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - onnx |
| - text-classification |
| - prompt-injection |
| - deberta |
| pipeline_tag: text-classification |
| --- |
| |
| # pi-detector — DeBERTa-v3 Prompt Injection Detector (ONNX) |
|
|
| FP32 and FP16 ONNX exports of [`protectai/deberta-v3-base-prompt-injection-v2`](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2), built for in-browser inference with [Transformers.js](https://github.com/huggingface/transformers.js). |
|
|
| ## Files |
|
|
| | File | Size | Precision | Use | |
| |------|------|-----------|-----| |
| | `onnx/model.onnx` | 738 MB | FP32 | WASM / CPU fallback | |
| | `onnx/model_fp16.onnx` | 370 MB | FP16 | WebGPU | |
|
|
| The FP16 export was produced with `onnxconverter-common` (`convert_float_to_float16`, `keep_io_types=True`, shape inference disabled) plus a type-directed repair pass: 77 `Cast` nodes were inserted at FP16/FP32 boundaries (relative-position constants and mask paths stay FP32) and stale `value_info` entries were stripped. Logits match FP32 to ~0.6% relative error with identical argmax on all tested samples. |
|
|
| > Note: the FP16 model requires WebGPU (or an ORT build with FP16 support). The WASM EP of onnxruntime-web currently fails LayerNorm fusion on this graph, hence the FP32 copy for WASM. |
|
|
| ## Usage (browser) |
|
|
| ```js |
| import { pipeline } from '@huggingface/transformers'; |
| |
| const classifier = await pipeline('text-classification', 'ASD492/pi-detector', { |
| device: 'webgpu', // falls back to 'wasm' + fp32 if unavailable |
| dtype: 'fp16', |
| }); |
| |
| const out = await classifier('Ignore previous instructions...'); |
| // [{ label: 'INJECTION', score: 0.9999 }] |
| ``` |
|
|
| ## Labels |
|
|
| - `SAFE` — normal instruction or question |
| - `INJECTION` — prompt injection attempt |
|
|
