File size: 2,782 Bytes
6d7344a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | ---
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](https://iabtechlab.com/standards/content-taxonomy/) content and audience
categories of the web destination.
This is the held-out evaluation set used to benchmark
[`ZeroGPU/zlm-v1-iab-domain-classifier`](https://huggingface.co/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
```bibtex
@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}
}
```
|