---
title: wiki_ner
canonical_url: "https://www.modelscope.ai/datasets/iic/wiki_ner"
md_url: "https://www.modelscope.ai/datasets/iic/wiki_ner.md"
repository: iic/wiki_ner
last_updated: 2025-09-20
license: other
storage_size: "93 KB"
domain:
  - text
tasks:
  - token-classification
language:
  - en
type:
  - ner
downloads: 128
stars: 0
---

# wiki_ner

> wiki_ner - An open-source dataset by iic on ModelScope. WikiDiverseNER命名实体识别数据集

iic/wiki_ner is a token-classification dataset on ModelScope. covering text. totalling 93 KB. licensed under other.

- **Repository**: iic/wiki_ner
- **License**: other
- **Tasks**: token-classification
- **Domain**: text
- **Storage size**: 93 KB
- **Downloads**: 128
- **Stars**: 0
- **Last updated**: 2025-09-20

Source: https://www.modelscope.ai/datasets/iic/wiki_ner

---

# WikiDiverseNER命名实体识别数据集

## 数据集概述
WikiDiverseNER数据集是基于WikiDiverse实体链接数据集构造的NER数据集。

### 数据集简介
本数据集包括测试集（757），实体类型包括应用(APP)、书籍(Book)、建筑(Building)、国家(Country)、货币(Currency)、事件(Event)、地址(Location)、电影(Movie)、音乐(Music)、组织(Organization)、其他(Other)、人物(People)、景点(Site)。

### 数据集的格式和结构
数据格式采用conll标准，数据分为两列，第一列是输入句中的词划分，第二列是每个词对应的命名实体类型标签。一个具体case的例子如下：

```
10	O
Chris	B-People
Bond	I-People
,	O
11	O
Ryan	B-People
Scott	I-People
and	O
teammates	O
during	O
a	O
time	O
out	O
.	O
```

## 数据集版权信息

Creative Commons Attribution 4.0 International。

## 引用方式
  ```bib
    @inproceedings{wang-etal-2022-wikidiverse,
        title = "{W}iki{D}iverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types",
        author = "Wang, Xuwu  and
        Tian, Junfeng  and
        Gui, Min  and
        Li, Zhixu  and
        Wang, Rui  and
        Yan, Ming  and
        Chen, Lihan  and
        Xiao, Yanghua",
        booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
        month = may,
        year = "2022",
        address = "Dublin, Ireland",
        publisher = "Association for Computational Linguistics",
        url = "https://aclanthology.org/2022.acl-long.328",
        doi = "10.18653/v1/2022.acl-long.328",
        pages = "4785--4797",
    }
  ```