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README.md

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+---
+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