Datasets:

ZeroGPU
/

iab-url-gold-784

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
size_categories:
  - n<1K
pretty_name: IAB URL Gold 784
tags:
  - iab-taxonomy
  - url-classification
  - domain-classification
  - benchmark
  - multi-label

IAB URL Gold 784

A 784-row gold benchmark for URL-only IAB classification: given nothing but a bare domain (rendered as a short text string), predict the IAB Tech Lab content and audience categories of the web destination.

This is the held-out evaluation set used to benchmark ZeroGPU/zlm-v1-iab-domain-classifier against GPT-5.4-nano. On it, the fine-tuned 149M ModernBERT model reaches content micro-F1 0.3845 (P 0.4247 / R 0.3512) vs the nano baseline's 0.3526 (P 0.3332 / R 0.3744), evaluated identically.

Schema

One JSON object per line in data/gold.jsonl:

Field	Type	Description
`id`	string	Stable row id
`text`	string	The model input: `"<host> \| <segmented words> \| tld:<tld>"` (e.g. `"espn.com \| espn \| tld:com"`)
`content_labels`	list[string]	Gold IAB Content taxonomy labels
`audience_labels`	list[string]	Gold IAB Audience taxonomy labels (pipe-delimited paths)
`strat_tier1`	string	IAB content tier-1 category used for stratification

Provenance

Domains were drawn from large public domain rankings, stratified across IAB content tier-1 categories.
Gold labels were produced by GPT-5.5 acting as an independent referee — a stronger model than either system evaluated on this set, and not the producer of the classifier's training labels, so the benchmark does not favor the fine-tuned model.
All 784 rows were excluded (by id and text) from the classifier's training corpus.

Evaluation protocol

Score content micro-F1 against content_labels: for each row take the system's predicted content labels (top-k = 6 for free-form LLM baselines; calibrated score-threshold cut for the classifier), accumulate true/false positives and false negatives over all rows, and compute precision, recall, and F1. Rows with empty content_labels are skipped.

Considerations

Labels are model-generated (GPT-5.5), not human-annotated; treat them as high-quality silver-standard gold.
The taxonomy includes sensitive categories (health, religion, adult content). The rows describe web destinations, not people.

Citation

@misc{zerogpu2026iaburlgold784,
  title  = {IAB URL Gold 784: a gold benchmark for URL-only IAB classification},
  author = {ZeroGPU},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ZeroGPU/iab-url-gold-784}
}