README.md

metadata

license: apache-2.0
base_model: Alibaba-NLP/gte-modernbert-base
pipeline_tag: text-classification
language:
  - en
library_name: onnx
tags:
  - iab-taxonomy
  - url-classification
  - domain-classification
  - contextual-targeting
  - brand-safety
  - multi-label-classification
  - modernbert
  - onnx
model-index:
  - name: zlm-v1-iab-domain-classifier
    results:
      - task:
          type: text-classification
          name: URL-only IAB content classification (multi-label)
        dataset:
          name: 784-URL held-out gold benchmark (independent GPT-5.5 labels)
          type: ZeroGPU/iab-url-gold-784
        metrics:
          - type: f1
            name: Content micro-F1 (calibrated)
            value: 0.3845
          - type: precision
            name: Content micro-precision
            value: 0.4247
          - type: recall
            name: Content micro-recall
            value: 0.3512

zlm-v1-iab-domain-classifier

A 149M-parameter ModernBERT model that classifies a web destination into IAB content and audience categories from its URL alone — no page fetch, no page text.

Code: github.com/zerogpu/zlm-v1-iab-domain-classifier · Benchmark dataset: ZeroGPU/iab-url-gold-784

Given nothing but a domain such as espn.com, the model returns standard IAB Tech Lab content and audience taxonomy categories. On a held-out, independently-labeled benchmark it is more accurate than GPT-5.4-nano on the same task while running ~50× faster (~36 ms vs ~1,900 ms median per URL).

Typical uses: contextual ad targeting, brand-safety screening, and audience enrichment in settings where only the URL is available — including dead, parked, or un-crawlable domains where there is no page to read.

Model description

• Backbone: Alibaba-NLP/gte-modernbert-base (ModernBERT, 149M parameters), fine-tuned end-to-end.
• Heads: two multi-label MLP heads over the encoder — IAB Content (704 labels) and IAB Audience (1,567 labels).
• Loss: Asymmetric Loss (ASL), sequence length 512.
• Calibration: per-head decision thresholds tuned on a validation split (content default 0.60, audience default 0.50, with per-tier-1 overrides in thresholds.json). Calibration lifted content precision from ~0.26 to ~0.42 and is a large part of the accuracy win.
• Formats: fp32 ONNX (onnx/model.onnx), int8-quantized ONNX (onnx/model_quantized.onnx), and the original PyTorch checkpoint (model.safetensors).

Input format

The model is trained and served on a deterministic string rendering of the URL, not the raw URL. Reproduce it byte-for-byte at inference time:

"<host> | <segmented words> | tld:<tld>"

Examples:

https://www.sportsnews.co.uk/foo  ->  "sportsnews.co.uk | sports news | tld:co.uk"
espn.com                          ->  "espn.com | espn | tld:com"

The transformation: lowercase; strip scheme/www./port/path/query; split the host on ./-/digits; peel the TLD (public suffix) off as its own tld: token; word-segment glued labels (sportsnews → sports news, via wordninja) so the word-piece encoder sees real words. A reference implementation (url_text.py) is included in this repository.

Usage (Python, onnxruntime)

import json
import numpy as np
import onnxruntime as ort
from tokenizers import Tokenizer
from url_text import url_to_text  # reference implementation in this repo

sess = ort.InferenceSession("onnx/model_quantized.onnx")
tok = Tokenizer.from_file("tokenizer.json")
meta = json.load(open("multi_head_classifier_metadata.json"))
thresholds = json.load(open("thresholds.json"))

text = url_to_text("https://www.espn.com")   # "espn.com | espn | tld:com"
enc = tok.encode(text)
outputs = sess.run(None, {
    "input_ids": np.array([enc.ids], dtype=np.int64),
    "attention_mask": np.array([enc.attention_mask], dtype=np.int64),
})

# One output per head (see meta["categories"] for order); apply sigmoid and the
# calibrated per-head threshold, then map indices to names via meta["label_mappings"].
def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))

for head, logits in zip(meta["categories"], outputs):
    probs = sigmoid(logits[0])
    cut = thresholds[head]["_default"]
    labels = meta["label_mappings"]["category_labels"][head]
    picked = [(labels[i], float(p)) for i, p in enumerate(probs) if p >= cut]
    print(head, sorted(picked, key=lambda t: -t[1])[:6])

The same model runs in Node.js via onnxruntime-node (the tokenizer files are standard Hugging Face tokenizers JSON).

Evaluation

Task: URL-only multi-label IAB content classification. Benchmark set: 784 held-out URLs the model never saw in training, spanning the full breadth of IAB tier-1 categories. Benchmark rows were excluded from the training corpus by both id and text. The set is published at ZeroGPU/iab-url-gold-784. Ground truth: every URL independently labeled by GPT-5.5 — a stronger model than either system under test, and not the producer of the training labels, so it does not favor the fine-tuned model. Baseline: GPT-5.4-nano prompted to do the same URL-to-IAB task. Metric: content micro-F1; both systems scored identically against the gold labels.

Headline result

	This model (calibrated)	GPT-5.4-nano
Content micro-F1	0.3845	0.3526
Precision	0.4247	0.3332
Recall	0.3512	0.3744
Median latency / URL	~36 ms	~1,900 ms
Off-taxonomy labels	0 (closed label set)	possible (free-form)

The model wins by +0.032 F1 (≈ +9%), driven by a large precision gain at roughly equal recall: when it assigns a category, it is right more often.

Encoder ablation

The same fine-tune recipe was A/B-tested on two backbones (uncalibrated, F1@6 on the same 784-row gold set):

Backbone	Gold F1@6	Precision	Recall
all-MiniLM-L6-v2	0.2623	0.2066	0.3589
gte-modernbert-base	0.3332	0.2625	0.4561

MiniLM plateaued well below the nano baseline; ModernBERT cleared it once calibrated.

Per-category results (IAB tier-1)

The model beats GPT-5.4-nano in 18 of the 26 well-populated tier-1 categories, with the largest wins in commercially important verticals (Shopping, Technology & Computing, Business & Finance, Sports, Movies). It trails mostly in small, ambiguous, or naming-driven categories where a bare domain under-determines the topic.

Full per-tier-1 table (784-row gold set)

Tier-1 category	Rows	Model F1	Nano F1	Δ
Pets	3	0.4706	0.1600	+0.3106
Healthy Living	10	0.4444	0.2529	+0.1916
Shopping	49	0.5525	0.4038	+0.1487
Unknown	8	0.2308	0.0976	+0.1332
Movies	11	0.7213	0.5902	+0.1311
Technology & Computing	62	0.5514	0.4228	+0.1286
Science	6	0.3333	0.2128	+0.1206
Television	9	0.5000	0.3939	+0.1061
Sports	28	0.4795	0.3756	+0.1039
Personal Finance	37	0.3012	0.2098	+0.0914
Business and Finance	108	0.3697	0.2861	+0.0836
Home & Garden	22	0.5000	0.4492	+0.0508
Travel	21	0.4189	0.3708	+0.0481
Careers	15	0.3670	0.3279	+0.0391
Hobbies & Interests	10	0.5172	0.4789	+0.0384
Video Gaming	19	0.3740	0.3357	+0.0383
Sensitive Topics	20	0.2500	0.2192	+0.0308
Education	54	0.4160	0.3883	+0.0277
Fine Art	7	0.4878	0.4762	+0.0116
Music and Audio	8	0.3333	0.3226	+0.0108
News and Politics	15	0.1600	0.1509	+0.0091
Automotive	37	0.3084	0.3493	−0.0409
Family and Relationships	9	0.2593	0.3030	−0.0438
Food & Drink	29	0.2588	0.3163	−0.0575
Style & Fashion	73	0.3629	0.4275	−0.0645
Books and Literature	9	0.4828	0.5714	−0.0887
Medical Health	38	0.2500	0.3410	−0.0910
Events and Attractions	50	0.1193	0.2787	−0.1594
Religion & Spirituality	13	0.3889	0.5610	−0.1721
Real Estate	4	0.1600	0.3529	−0.1929

Training data

The model was trained on ~1 million domains whose IAB categories were labeled by a stronger teacher LLM that could see the live page (title and description) at labeling time. The model distills that page-aware knowledge into the URL string — at run time, from the URL alone, it reproduces judgments that normally require fetching the page. This privileged-information distillation is why it can exceed a same-class LLM that only sees the bare URL. A smaller independently-labeled gold set was held out for validation and threshold calibration.

Limitations and intended use

• Absolute F1 is modest (~0.38) because the task is hard: a bare domain often genuinely under-determines the topic. Treat outputs as probabilistic enrichment signals, not ground truth about a site.
• Accuracy is not uniform across categories — see the per-category table. It is weakest on Events & Attractions, Religion & Spirituality, Real Estate, and Medical Health.
• The model can only emit labels from the fixed IAB taxonomy snapshot it was trained on (704 content + 1,567 audience labels).
• Trained predominantly on ASCII/English-tokenizable domains; non-Latin or punycode-heavy domains will see degraded quality.
• The taxonomy includes sensitive categories (e.g. health, religion, adult content). Do not use the model to infer sensitive attributes of individual people; it is built for classifying web destinations for contextual advertising use cases, and predictions in sensitive categories should be reviewed before being acted on.

License and attribution

• Model weights: Apache-2.0 (same as the base model, Alibaba-NLP/gte-modernbert-base).
• Built on ModernBERT (Answer.AI / LightOn).
• Label space follows the IAB Tech Lab Content & Audience Taxonomies, © IAB Technology Laboratory.

Citation

@misc{zerogpu2026iabdomainclassifier,
  title  = {zlm-v1-iab-domain-classifier: URL-only IAB classification with a fine-tuned ModernBERT},
  author = {ZeroGPU},
  year   = {2026},
  url    = {https://huggingface.co/ZeroGPU/zlm-v1-iab-domain-classifier}
}