Nerkor Cars Onpp Hubert

🚀 匈牙利命名实体识别模型（OntoNotes5 + 更多实体类型）

本模型是用于匈牙利语命名实体识别的模型，基于预训练模型微调而来，能识别多种实体类型，在相关语料上有良好表现。

🚀 快速开始

• 预训练模型：SZTAKI - HLT/hubert - base - cc
• 在NerKor + CARS - ONPP语料库上进行微调

🔧 技术细节

限制条件

• 最大序列长度 = 448

训练数据

基础语料 [NerKor + CARS - OntoNotes++](https://github.com/ppke - nlpg/NYTK - NerKor - Cars - OntoNotesPP) 源自 [NYTK - NerKor](https://github.com/nytud/NYTK - NerKor)，这是一个匈牙利语的黄金标准命名实体标注语料库，包含约100万个标记。它还额外添加了来自 hvg.hu 新闻存档中关于机动车（汽车、公交车、摩托车）的1.2万个标记的文本（单个句子）。

NYTK - NerKor 的标注遵循CoNLL2002标注标准，只有四种命名实体类别（PER、LOC、MISC、ORG），而此版本的语料库有超过30种实体类型，包括 [OntoNotes 5.0] 英语命名实体识别标注中使用的所有实体类型。新的标注详细说明了 LOC 和 MISC 实体类型的子类型，并对非名称（如时间和日期、数量、语言和国籍或宗教或政治团体）进行了标注。此外，标注还细化了OntoNotes 5标注中不存在的其他实体子类型（见下文）。

源自OntoNotes 5.0标注的标签

名称根据以下类型集进行标注：

以下也以类似名称的方式进行标注：

额外标签（OntoNotes 5中没有）

类型为 MISC 的名称的其他子类型

其他非名称实体

📄 许可证

本项目采用GPL许可证。

引用说明

如果您使用此模型，请引用以下文献：

@inproceedings{novak-novak-2022-nerkor,
    title = "{N}er{K}or+{C}ars-{O}nto{N}otes++",
    author = "Nov{\'a}k, Attila  and
      Nov{\'a}k, Borb{\'a}la",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.203",
    pages = "1907--1916",
    abstract = "In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.",
}