Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: few-nerd
pretty_name: Few-NERD
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
Dataset Card for "Few-NERD"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://ningding97.github.io/fewnerd/
- Repository: https://github.com/thunlp/Few-NERD
- Paper: https://aclanthology.org/2021.acl-long.248/
- Point of Contact: See 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 MBintra: 11.4 MBinter: 11.5 MB
Size of the generated dataset:
super: 116.9 MBintra: 106.2 MBinter: 106.2 MB
Total amount of disk used: 366.8 MB
An example of 'train' looks as follows.
{
'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: astringfeature.tokens: alistofstringfeatures.ner_tags: alistof classification labels, with possible values includingO(0),art(1),building(2),event(3),location(4),organization(5),other(6),person(7),product(8)fine_ner_tags: alistof fine-grained classification labels, with possible values includingO(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
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
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",
}