metadata
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.