sdd-distiller

sdd-distiller-v1

Semantic DOM Distiller — An ONNX model that scores the importance of each DOM node from 0.0 to 1.0.

GitHub: watilde/sdd | Live Demo

Overview

Modern websites have extremely complex and obfuscated DOM structures. Feeding raw DOM trees into LLMs leads to inflated token costs and degraded reasoning accuracy due to noise.

sdd-distiller-v1 is the core scoring model of the Semantic DOM Distiller (SDD) pipeline — a DOM-to-Specification preprocessing engine optimized for multimodal AI agents such as Amazon Nova Act.

Given a 41-dimensional feature vector representing a DOM node, the model outputs an importance score between 0.0 and 1.0. Nodes below a configurable threshold are pruned, reconstructing a lean "functional DOM tree" that contains only what the AI needs to understand the page.

Model Details

Input Features (41 dimensions)

Usage

Node.js (onnxruntime-node)

import * as ort from 'onnxruntime-node';

const session = await ort.InferenceSession.create('./sdd-distiller-v1.onnx');

// Build a 41-dim feature vector (Float32Array) for a DOM node
const featureVector = new Float32Array(41);
// featureVector[0] = isHighValueTag, featureVector[3] = isInteractive, ...

const input = new ort.Tensor('float32', featureVector, [1, 41]);
const result = await session.run({ input });
const score = result.output.data[0]; // 0.0 ~ 1.0

Browser (onnxruntime-web)

import * as ort from 'onnxruntime-web';

ort.env.wasm.wasmPaths = 'https://cdn.jsdelivr.net/npm/[email protected]/dist/';
const session = await ort.InferenceSession.create('./sdd-distiller-v1.onnx');

const featureVector = new Float32Array(41);
const input = new ort.Tensor('float32', featureVector, [1, 41]);
const result = await session.run({ input });
const score = result.output.data[0]; // 0.0 ~ 1.0

Python (onnxruntime)

import onnxruntime as rt
import numpy as np

sess = rt.InferenceSession("sdd-distiller-v1.onnx")

# Build a (1, 41) float32 feature matrix
feature_vector = np.zeros((1, 41), dtype=np.float32)
# feature_vector[0, 0] = 1.0  # isHighValueTag
# feature_vector[0, 3] = 1.0  # isInteractive
# ...

score = sess.run(["output"], {"input": feature_vector})[0][0]
print(f"Importance score: {score:.3f}")  # 0.0 ~ 1.0

Training

• Training data: 50,000 synthetic samples generated by using the heuristic scorer as a teacher
• Base learner: sklearn.ensemble.GradientBoostingRegressor (200 estimators, max_depth=5, lr=0.05)
• Export tool: skl2onnx with ONNX opset 17
• Training script: scripts/train_model.py

Top Feature Importances

Links

• 💻 GitHub: https://github.com/watilde/sdd
• 🌐 Live Demo: https://watilde.github.io/sdd/
• 📦 npm: semantic-dom-distiller (coming soon)