| --- |
| license: mit |
| language: en |
| pipeline_tag: text-classification |
| tags: |
| - ai-text-detection |
| - deberta-v3 |
| - onnx |
| - webgpu |
| - raid |
| library_name: onnx |
| --- |
| |
| # SLOP — AI-text detector (DeBERTa-v3 + FeatAttn) |
|
|
| Sub-200M-parameter detector for machine-generated text. DeBERTa-v3-base encoder |
| fused with a Feature-Attention head over 17 surface stylometric features |
| (readability, type-token ratio, sentence-length CV, …). **No reference LM** at |
| inference — runs fully self-contained, including in the browser on WebGPU. |
|
|
| - **Params:** 184.2M (embeddings 98.8M / encoder 85.1M / heads 0.36M) |
| - **Training:** HC3 + ai-text-detection-pile + RAID train (in-distribution), |
| focal loss (γ=2.0, α=0.85) |
| - **Inputs:** `input_ids [B,S]`, `attention_mask [B,S]`, `features [B,17]` |
| - **Output:** `prob [B]` = P(AI) (sigmoid already applied) |
|
|
| ## Files |
| | file | notes | |
| | --- | --- | |
| | `model.safetensors` | fp32 weights | |
| | `onnx/model_fp32.onnx` | full precision | |
| | `onnx/model_fp16.onnx` | half precision | |
| | `onnx/model_int8.onnx` | 8-bit dynamic | |
| | `onnx/model_q4.onnx` | **4-bit (MatMulNBits) + int8 embeddings — for WebGPU/onnxruntime-web** | |
|
|
| ## Browser usage (onnxruntime-web, WebGPU) |
| The `q4` graph is `MatMulNBits` 4-bit matmuls + an int8-quantised embedding table |
| (~170 MB). Feed `input_ids`/`attention_mask` from the DeBERTa tokenizer and the |
| 17-d feature vector; the graph returns `prob`. |
|
|
| Built with [slopdetector](https://github.com/anudit/slopdetector). |
|
|
