rishabhraj1010/few-nerd
收藏Hugging Face2026-04-13 更新2026-04-26 收录
下载链接：
https://hf-mirror.com/datasets/rishabhraj1010/few-nerd
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资源简介：
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: few-nerd
pretty_name: Few-NERD
tags:
- structure-prediction
dataset_info:
- config_name: inter
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': art
'2': building
'3': event
'4': location
'5': organization
'6': other
'7': person
'8': product
- name: fine_ner_tags
sequence:
class_label:
names:
'0': O
'1': art-broadcastprogram
'2': art-film
'3': art-music
'4': art-other
'5': art-painting
'6': art-writtenart
'7': building-airport
'8': building-hospital
'9': building-hotel
'10': building-library
'11': building-other
'12': building-restaurant
'13': building-sportsfacility
'14': building-theater
'15': event-attack/battle/war/militaryconflict
'16': event-disaster
'17': event-election
'18': event-other
'19': event-protest
'20': event-sportsevent
'21': location-GPE
'22': location-bodiesofwater
'23': location-island
'24': location-mountain
'25': location-other
'26': location-park
'27': location-road/railway/highway/transit
'28': organization-company
'29': organization-education
'30': organization-government/governmentagency
'31': organization-media/newspaper
'32': organization-other
'33': organization-politicalparty
'34': organization-religion
'35': organization-showorganization
'36': organization-sportsleague
'37': organization-sportsteam
'38': other-astronomything
'39': other-award
'40': other-biologything
'41': other-chemicalthing
'42': other-currency
'43': other-disease
'44': other-educationaldegree
'45': other-god
'46': other-language
'47': other-law
'48': other-livingthing
'49': other-medical
'50': person-actor
'51': person-artist/author
'52': person-athlete
'53': person-director
'54': person-other
'55': person-politician
'56': person-scholar
'57': person-soldier
'58': product-airplane
'59': product-car
'60': product-food
'61': product-game
'62': product-other
'63': product-ship
'64': product-software
'65': product-train
'66': product-weapon
splits:
- name: train
num_bytes: 87456461
num_examples: 130112
- name: validation
num_bytes: 10813084
num_examples: 18817
- name: test
num_bytes: 7920453
num_examples: 14007
download_size: 19914244
dataset_size: 106189998
- config_name: intra
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': art
'2': building
'3': event
'4': location
'5': organization
'6': other
'7': person
'8': product
- name: fine_ner_tags
sequence:
class_label:
names:
'0': O
'1': art-broadcastprogram
'2': art-film
'3': art-music
'4': art-other
'5': art-painting
'6': art-writtenart
'7': building-airport
'8': building-hospital
'9': building-hotel
'10': building-library
'11': building-other
'12': building-restaurant
'13': building-sportsfacility
'14': building-theater
'15': event-attack/battle/war/militaryconflict
'16': event-disaster
'17': event-election
'18': event-other
'19': event-protest
'20': event-sportsevent
'21': location-GPE
'22': location-bodiesofwater
'23': location-island
'24': location-mountain
'25': location-other
'26': location-park
'27': location-road/railway/highway/transit
'28': organization-company
'29': organization-education
'30': organization-government/governmentagency
'31': organization-media/newspaper
'32': organization-other
'33': organization-politicalparty
'34': organization-religion
'35': organization-showorganization
'36': organization-sportsleague
'37': organization-sportsteam
'38': other-astronomything
'39': other-award
'40': other-biologything
'41': other-chemicalthing
'42': other-currency
'43': other-disease
'44': other-educationaldegree
'45': other-god
'46': other-language
'47': other-law
'48': other-livingthing
'49': other-medical
'50': person-actor
'51': person-artist/author
'52': person-athlete
'53': person-director
'54': person-other
'55': person-politician
'56': person-scholar
'57': person-soldier
'58': product-airplane
'59': product-car
'60': product-food
'61': product-game
'62': product-other
'63': product-ship
'64': product-software
'65': product-train
'66': product-weapon
splits:
- name: train
num_bytes: 67631522
num_examples: 99519
- name: validation
num_bytes: 12759787
num_examples: 19358
- name: test
num_bytes: 25768577
num_examples: 44059
download_size: 19616006
dataset_size: 106159886
- config_name: supervised
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': art
'2': building
'3': event
'4': location
'5': organization
'6': other
'7': person
'8': product
- name: fine_ner_tags
sequence:
class_label:
names:
'0': O
'1': art-broadcastprogram
'2': art-film
'3': art-music
'4': art-other
'5': art-painting
'6': art-writtenart
'7': building-airport
'8': building-hospital
'9': building-hotel
'10': building-library
'11': building-other
'12': building-restaurant
'13': building-sportsfacility
'14': building-theater
'15': event-attack/battle/war/militaryconflict
'16': event-disaster
'17': event-election
'18': event-other
'19': event-protest
'20': event-sportsevent
'21': location-GPE
'22': location-bodiesofwater
'23': location-island
'24': location-mountain
'25': location-other
'26': location-park
'27': location-road/railway/highway/transit
'28': organization-company
'29': organization-education
'30': organization-government/governmentagency
'31': organization-media/newspaper
'32': organization-other
'33': organization-politicalparty
'34': organization-religion
'35': organization-showorganization
'36': organization-sportsleague
'37': organization-sportsteam
'38': other-astronomything
'39': other-award
'40': other-biologything
'41': other-chemicalthing
'42': other-currency
'43': other-disease
'44': other-educationaldegree
'45': other-god
'46': other-language
'47': other-law
'48': other-livingthing
'49': other-medical
'50': person-actor
'51': person-artist/author
'52': person-athlete
'53': person-director
'54': person-other
'55': person-politician
'56': person-scholar
'57': person-soldier
'58': product-airplane
'59': product-car
'60': product-food
'61': product-game
'62': product-other
'63': product-ship
'64': product-software
'65': product-train
'66': product-weapon
splits:
- name: train
num_bytes: 81848645
num_examples: 131767
- name: validation
num_bytes: 11731110
num_examples: 18824
- name: test
num_bytes: 23345314
num_examples: 37648
download_size: 24121858
dataset_size: 116925069
configs:
- config_name: inter
data_files:
- split: train
path: inter/train-*
- split: validation
path: inter/validation-*
- split: test
path: inter/test-*
- config_name: intra
data_files:
- split: train
path: intra/train-*
- split: validation
path: intra/validation-*
- split: test
path: intra/test-*
- config_name: supervised
data_files:
- split: train
path: supervised/train-*
- split: validation
path: supervised/validation-*
- split: test
path: supervised/test-*
---
# Dataset Card for "Few-NERD"
## Table of Contents
- [Dataset Description](
#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
- **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
- **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
### Dataset Summary
This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).
NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.
For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition, Few-shot NER
- **Leaderboards:**
- https://ningding97.github.io/fewnerd/
- named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter
### Languages
English
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:**
- `super`: 14.6 MB
- `intra`: 11.4 MB
- `inter`: 11.5 MB
- **Size of the generated dataset:**
- `super`: 116.9 MB
- `intra`: 106.2 MB
- `inter`: 106.2 MB
- **Total amount of disk used:** 366.8 MB
An example of 'train' looks as follows.
```json
{
'id': '1',
'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'],
'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0],
'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8)
- `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ...
### Data Splits
| Task | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| SUP | 131767 | 18824 | 37648 |
| INTRA | 99519 | 19358 | 44059 |
| INTER | 130112 | 18817 | 14007 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{ding-etal-2021-nerd,
title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
author = "Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.248",
doi = "10.18653/v1/2021.acl-long.248",
pages = "3198--3213",
}
```
### Contributions
### 数据集元数据
- **标注创作者**: 专家生成
- **语言创建方式**: 公开采集
- **语言**: 英语
- **许可协议**: CC BY-SA 4.0
- **多语言属性**: 单语言
- **样本规模类别**: 10万 < 样本量 < 100万
- **源数据集**: 扩展|维基百科（extended|wikipedia）
- **任务类别**: Token分类（token-classification）
- **任务子项**: 命名实体识别（Named Entity Recognition, NER）
- **PapersWithCode标识**: few-nerd
- **展示名称**: Few-NERD
- **标签**: 结构预测（structure-prediction）
### 数据集详细配置信息
#### 配置项1: inter
- **配置名称**: inter
- **特征字段**:
1. `id`: 字符串类型特征
2. `tokens`: 字符串序列（Token序列）特征
3. `ner_tags`: 分类标签序列，类别映射为：0→O（非实体）、1→art（艺术实体）、2→building（建筑）、3→event（事件）、4→location（地理位置）、5→organization（组织机构）、6→other（其他实体）、7→person（人物）、8→product（产品）
4. `fine_ner_tags`: 细粒度分类标签序列，完整类别映射参见后文数据字段说明
- **数据划分**:
- 训练集: 130112个样本，占用字节数87456461
- 验证集: 18817个样本，占用字节数10813084
- 测试集: 14007个样本，占用字节数7920453
- **下载大小**: 19914244字节
- **数据集总占用大小**: 106189998字节
#### 配置项2: intra
- **配置名称**: intra
- **特征字段**: 与inter配置一致
- **数据划分**:
- 训练集: 99519个样本，占用字节数67631522
- 验证集: 19358个样本，占用字节数12759787
- 测试集: 44059个样本，占用字节数25768577
- **下载大小**: 19616006字节
- **数据集总占用大小**: 106159886字节
#### 配置项3: supervised
- **配置名称**: supervised
- **特征字段**: 与inter配置一致
- **数据划分**:
- 训练集: 131767个样本，占用字节数81848645
- 验证集: 18824个样本，占用字节数11731110
- 测试集: 37648个样本，占用字节数23345314
- **下载大小**: 24121858字节
- **数据集总占用大小**: 116925069字节
### 配置文件说明
本数据集包含三个配置项，对应数据文件路径如下：
1. **inter**: 训练集路径`inter/train-*`、验证集路径`inter/validation-*`、测试集路径`inter/test-*`
2. **intra**: 训练集路径`intra/train-*`、验证集路径`intra/validation-*`、测试集路径`intra/test-*`
3. **supervised**: 训练集路径`supervised/train-*`、验证集路径`supervised/validation-*`、测试集路径`supervised/test-*`
## 「Few-NERD」数据集卡片
## 目录
- [数据集描述](#数据集描述)
- [数据集概述](#数据集概述)
- [支持任务与排行榜](#支持任务与排行榜)
- [语言](#语言)
- [数据集结构](#数据集结构)
- [数据实例](#数据实例)
- [数据字段](#数据字段)
- [数据划分](#数据划分)
- [数据集构建](#数据集构建)
- [构建初衷](#构建初衷)
- [源数据](#源数据)
- [标注信息](#标注信息)
- [个人与敏感信息](#个人与敏感信息)
- [数据集使用注意事项](#数据集使用注意事项)
- [数据集的社会影响](#数据集的社会影响)
- [偏差讨论](#偏差讨论)
- [其他已知局限](#其他已知局限)
- [附加信息](#附加信息)
- [数据集策展人](#数据集策展人)
- [许可信息](#许可信息)
- [引用信息](#引用信息)
- [贡献](#贡献)
## 数据集描述
- **主页**: [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- **代码仓库**: [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
- **论文**: [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
- **联系方式**: 参见 [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
### 数据集概述
本脚本用于加载来自 [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) 的Few-NERD数据集。
Few-NERD是一款大规模、细粒度的人工标注命名实体识别（Named Entity Recognition, NER）数据集，涵盖8大类粗粒度实体类型、66类细粒度实体类型，总计188,200个句子、491,711个实体以及4,601,223个Token。该数据集构建了三类基准任务：1个监督学习任务（Few-NERD（SUP）），另外两个为少样本学习任务（Few-NERD（INTRA）与Few-NERD（INTER））。
NER标签采用IO标注方案（IO tagging scheme），原始数据采用两列的CoNLL格式，以空行分隔不同句子；由于句子均为随机排序，故未提供DOCSTART相关信息。
更多细节可参见 [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) 与 [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)。
### 支持任务与排行榜
- **任务**: 命名实体识别、少样本命名实体识别
- **排行榜**:
- [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- named-entity-recognition: [https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup](https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup)
- other-few-shot-ner: [https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra](https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra)
- other-few-shot-ner: [https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter](https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter)
### 语言
英语
## 数据集结构
### 数据实例
- **下载数据集文件大小**:
- `super`: 14.6 MB
- `intra`: 11.4 MB
- `inter`: 11.5 MB
- **生成数据集大小**:
- `super`: 116.9 MB
- `intra`: 106.2 MB
- `inter`: 106.2 MB
- **总磁盘占用**: 366.8 MB
「训练集」示例如下：
json
{
"id": "1",
"tokens": ["It", "starred", "Hicks", "'s", "wife", ",", "Ellaline", "Terriss", "and", "Edmund", "Payne", "."],
"ner_tags": [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0],
"fine_ner_tags": [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}
### 数据字段
所有划分的数据字段均保持一致：
- `id`: 字符串类型特征
- `tokens`: 字符串序列（Token序列）特征
- `ner_tags`: 分类标签列表，可选值包括`O`（0，非实体）、`art`（1，艺术相关实体）、`building`（2，建筑）、`event`（3，事件）、`location`（4，地理位置）、`organization`（5，组织机构）、`other`（6，其他实体）、`person`（7，人物）、`product`（8，产品）
- `fine_ner_tags`: 细粒度分类标签列表，可选值包括`O`（0）、`art-broadcastprogram`（1，广播节目）、`art-film`（2，影视）、`art-music`（3，音乐）、`art-other`（4，其他艺术实体）、`art-painting`（5，绘画）、`art-writtenart`（6，文字艺术作品）、`building-airport`（7，机场）、`building-hospital`（8，医院）、`building-hotel`（9，酒店）、`building-library`（10，图书馆）、`building-other`（11，其他建筑）、`building-restaurant`（12，餐厅）、`building-sportsfacility`（13，体育场馆）、`building-theater`（14，剧院）、`event-attack/battle/war/militaryconflict`（15，攻击/战役/战争/军事冲突）、`event-disaster`（16，灾害）、`event-election`（17，选举）、`event-other`（18，其他事件）、`event-protest`（19，抗议活动）、`event-sportsevent`（20，体育赛事）、`location-GPE`（21，地理政治实体）、`location-bodiesofwater`（22，水体）、`location-island`（23，岛屿）、`location-mountain`（24，山脉）、`location-other`（25，其他地理位置）、`location-park`（26，公园）、`location-road/railway/highway/transit`（27，道路/铁路/公路/交通设施）、`organization-company`（28，公司）、`organization-education`（29，教育机构）、`organization-government/governmentagency`（30，政府/政府机构）、`organization-media/newspaper`（31，媒体/报社）、`organization-other`（32，其他组织机构）、`organization-politicalparty`（33，政党）、`organization-religion`（34，宗教组织）、`organization-showorganization`（35，演出机构）、`organization-sportsleague`（36，体育联盟）、`organization-sportsteam`（37，运动队）、`other-astronomything`（38，天文事物）、`other-award`（39，奖项）、`other-biologything`（40，生物相关事物）、`other-chemicalthing`（41，化学相关事物）、`other-currency`（42，货币）、`other-disease`（43，疾病）、`other-educationaldegree`（44，学位）、`other-god`（45，神祇）、`other-language`（46，语言）、`other-law`（47，法律相关事物）、`other-livingthing`（48，生物）、`other-medical`（49，医疗相关事物）、`person-actor`（50，演员）、`person-artist/author`（51，艺术家/作家）、`person-athlete`（52，运动员）、`person-director`（53，导演）、`person-other`（54，其他人物）、`person-politician`（55，政治家）、`person-scholar`（56，学者）、`person-soldier`（57，军人）、`product-airplane`（58，飞机）、`product-car`（59，汽车）、`product-food`（60，食品）、`product-game`（61，游戏）、`product-other`（62，其他产品）、`product-ship`（63，船舶）、`product-software`（64，软件）、`product-train`（65，火车）、`product-weapon`（66，武器）
### 数据划分
| 任务 | 训练集 | 开发集 | 测试集 |
| :----- | :------ | :----- | :---- |
| SUP | 131767 | 18824 | 37648 |
| INTRA | 99519 | 19358 | 44059 |
| INTER | 130112 | 18817 | 14007 |
## 数据集构建
### 构建初衷
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 源数据
#### 初始数据收集与标准化
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### 源语言生产者是谁？
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 标注信息
#### 标注流程
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### 标注人员是谁？
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 个人与敏感信息
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 数据集使用注意事项
### 数据集的社会影响
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 偏差讨论
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 其他已知局限
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 附加信息
### 数据集策展人
[需补充更多信息](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### 许可信息
[CC BY-SA 4.0许可协议](https://creativecommons.org/licenses/by-sa/4.0/)
### 引用信息
@inproceedings{ding-etal-2021-nerd,
title = "Few-{NERD}: 少样本命名实体识别数据集",
author = "Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan",
booktitle = "第59届国际计算语言学协会年会及第11届自然语言处理国际联合会议论文集（卷1：长论文）",
month = aug,
year = "2021",
address = "线上",
publisher = "国际计算语言学协会",
url = "https://aclanthology.org/2021.acl-long.248",
doi = "10.18653/v1/2021.acl-long.248",
pages = "3198--3213",
}
### 贡献
提供机构：
rishabhraj1010
