zlm-v1-iab-classify-edge
ZeroGPU’s IAB classifier maps any text to the industry-standard IAB Content Taxonomy in a single, fast inference call. Each call returns categories across both the 1.0 and 2.2 taxonomies plus matched audience segments, every result scored for confidence. With support for 50+ languages, it classifies multilingual content without a translation step. At 90M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge. When you need clean category and audience signals on every request, this is the model — and when you need the full profile, reach for the enriched variant.References: Model docs • Terms • Privacy
audience categories plus content matches across taxonomy versions, each with a confidence score.
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zlm-v2-iab-classify-edge-enriched
The enriched variant of ZeroGPU’s IAB classifier turns a single inference call into a full content-intelligence profile not just a label, but everything a contextual pipeline needs to act on. Each call returns IAB categories across both the 1.0 and 2.2 taxonomies (down to tier-3), audience and interest segments, topics, keywords, and user-intent classification — all with confidence scores with edge-native, sub-millisecond speeds. With support for 50+ languages, it delivers the same full profile on multilingual content without a translation step. Built for contextual ad targeting, brand-safety scoring, publisher content categorization, and signal pipelines that need full metadata on every request.References: Model docs • Terms • Privacy
audience and content categories, the enriched model returns taxonomy codes and parent IDs, plus topics, keywords, and an inferred user_intent.
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zlm-v1-iab-domain-classifier
ZeroGPU’s Domain IAB Classifier maps a raw domain name straight to the IAB Content Taxonomy in a single, fast inference call, returning content categories, topics, keywords, and user-intent signals. Where the page-level IAB classifiers take the full body of an article as input, this model takes only the domain (for example nytimes.com), reducing payload size by up to 10x while still surfacing the contextual signals that bidstream enrichment, contextual targeting, and domain-level intelligence pipelines run on. At 149M parameters on ONNX, it’s built for the high-volume, sub-millisecond classification that ad tech and content platforms demand, right at the edge.
References: Model docs • Terms • Privacy
content categories across taxonomy versions plus topics, keywords, and an inferred user_intent for the domain.
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deberta-v3-small
Microsoft’s DeBERTa-v3-small is a fast, lightweight zero-shot text classifier for high-volume routing, filtering, and tagging. Hand it any text alongside your own candidate labels and it returns a confidence score for each. No training data and no fixed taxonomy, just the categories you define at request time. Because it scores every label rather than forcing one choice, you get soft probabilities to threshold, rank, or route on however your pipeline needs. Purpose-built and CPU-optimized, it runs faster and cheaper than asking a general-purpose frontier model to classify.References: Model docs • Terms • Privacy
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