AI正在发生 未来正在更新zlm-v1-iab-classify-edge
**A 24M-parameter MiniLM model that tags English text with IAB content and audience categories, small enough to run on the device: a 25 MB int8 ONNX file that classifies a 128-token text in 4–10 ms on a laptop CPU.** Give it an article, a page summary, or any short English text and it returns IAB Tech Lab **content** categories (704 labels, IAB Content Taxonomy 3.0) and **audience** segments (1,567 labels, IAB Audience Taxonomy 1.1) with a score for each. In a blind, 10,000-text comparison judged by GPT-5.5, its labels were preferred over **GPT-5.4-nano**'s in **66% of decided cases**, and on independent human labels it scores **0.406 F1 vs 0.375** for nano. It never returns a label outside the taxonomy. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android) in production. It is laid out for transformers.js and also runs directly in onnxruntime.
译文尚未就绪，显示来源原文
**A 24M-parameter MiniLM model that tags English text with IAB content and audience categories, small enough to run on the device: a 25 MB int8 ONNX file that classifies a 128-token text in 4–10 ms on a laptop CPU.** Give it an article, a page summary, or any short English text and it returns IAB Tech Lab **content** categories (704 labels, IAB Content Taxonomy 3.0) and **audience** segments (1,567 labels, IAB Audience Taxonomy 1.1) with a score for each. In a blind, 10,000-text comparison judged by GPT-5.5, its labels were preferred over **GPT-5.4-nano**'s in **66% of decided cases**, and on independent human labels it scores **0.406 F1 vs 0.375** for nano. It never returns a label outside the taxonomy. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android) in production. It is laid out for transformers.js and also runs directly in onnxruntime.