MODEL PROFILE · 模型档案

deberta-v3-large-ontonotes5

tner · deberta-v3-large-ontonotes5

This model is a fine-tuned version of microsoft/deberta-v3-large on the tner/ontonotes5 dataset. Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.9069623608411381 - Precision (micro): 0.902100360312857 - Recall (micro): 0.9118770542773386 - F1 (macro): 0.834586960779896 - Precision (macro): 0.8237351069457466 - Recall (macro): 0.8475169311172334 The per-entity breakdown of the F1 score on the test set are below: - cardinal_number: 0.853475935828877 - date: 0.8815545959284392 - event: 0.8030303030303031 - facility: 0.7896678966789669 - geopolitical_area: 0.9650033867690223 - group: 0.9337209302325581 - language: 0.8372093023255814 - law: 0.6756756756756757 - location: 0.7624020887728459 - money: 0.8818897637795275 - ordinal_number: 0.8635235732009926 - organization: 0.914952751528627 - percent: 0.9 - person: 0.9609866599546942 - product: 0.7901234567901234 - quantity: 0.8161434977578474 - time: 0.674364896073903 - work_of_art: 0.7188405797101449 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442] - F1 (macro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442]

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档案更新时间2026/10/30 11:05:18
OVERVIEW

模型介绍

This model is a fine-tuned version of microsoft/deberta-v3-large on the tner/ontonotes5 dataset. Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.9069623608411381 - Precision (micro): 0.902100360312857 - Recall (micro): 0.9118770542773386 - F1 (macro): 0.834586960779896 - Precision (macro): 0.8237351069457466 - Recall (macro): 0.8475169311172334 The per-entity breakdown of the F1 score on the test set are below: - cardinal_number: 0.853475935828877 - date: 0.8815545959284392 - event: 0.8030303030303031 - facility: 0.7896678966789669 - geopolitical_area: 0.9650033867690223 - group: 0.9337209302325581 - language: 0.8372093023255814 - law: 0.6756756756756757 - location: 0.7624020887728459 - money: 0.8818897637795275 - ordinal_number: 0.8635235732009926 - organization: 0.914952751528627 - percent: 0.9 - person: 0.9609866599546942 - product: 0.7901234567901234 - quantity: 0.8161434977578474 - time: 0.674364896073903 - work_of_art: 0.7188405797101449 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442] - F1 (macro): - 90%: [0.9019409960743083, 0.911751130722053] - 95%: [0.9010822890967028, 0.9125611412371442]

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:来源未披露,须进一步核验。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。