voidly-measurement-classifier-v1

emperor-mew/voidly-measurement-classifier-v1

voidly-measurement-classifier-v1 is a tabular classification model from emperor-mew. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for xgboost. The card lists the license as cc-by-4.0.

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Updated May 22, 2026

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At a glance

Task: Tabular Classification
Library: xgboost
License: cc-by-4.0
Access: Public
Created: May 22, 2026
Updated: May 22, 2026
SHA: 05ba021d

Model card

From the Hugging Face model README

Voidly Atlas Per-Measurement Classifier v1

Version: v1 | License: CC BY 4.0

XGBoost row-level censorship classifier inspired by Niaki et al. KDD23. Trained on 84K evidence rows, stratified 80/20 split. AUC 1.0 honest caveat: the model reconstructs the labeling rule from signal_value + source patterns rather than discovering novel signal. Lives at POST /v1/measurement/classify.

Eval

Metric	Value
`held_out_auc`	1
`held_out_f1`	1
`held_out_precision`	1
`held_out_recall`	1
`min_source_auc`	1
`label_balance_overall`	0.1965

Features

signal_value
signal_baseline
risk_tier
obs_hour
obs_dow
obs_is_weekend
obs_month
signal_deviation
signal_ratio
has_artifact_hash
has_source_url
has_domain
has_asn
asn_filled
country_7d_rate
asn_7d_rate
country_domain_7d_rate
source_code
kind_code
country_code_code
continent_code
un_subregion_code
region_code_code
domain_category_code

Honest caveats

AUC 1.0 is NOT a generalization claim. The model reconstructs labeling rules from raw signal magnitudes — see top feature asn_7d_rate (81% gain).
Use as an interface layer (per-row score for downstream UIs), not as a discovery model.
v3.3 country-day classifier is the canonical research artifact; this is a row-level wrapper.

Citation

@misc{voidly_voidly_measurement_classifier_v1,
  title  = {Voidly Atlas: voidly-measurement-classifier-v1 (v1)},
  author = {Voidly},
  year   = {2026},
  url    = {https://huggingface.co/emperor-mew/voidly-measurement-classifier-v1},
  note   = {Open censorship-research ML stack. CC BY 4.0.}
}

Method foundation: Niaki et al. KDD23 — Massively Parallel Censorship Probing

Task: Tabular Classification
Library: xgboost
License: cc-by-4.0
Created: May 22, 2026
Updated: May 22, 2026