MODEL PROFILE · 模型档案

zlm-v1-iab-classify-edge

zgpu-ai · 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.

译文排队中,当前展示来源原文。

文本分析license:apache-2.0
上下文窗口来源未披露
输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:49
OVERVIEW

模型介绍

**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.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。