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Dataset Card for "Few-NERD"

Dataset Summary

This script is for loading the Few-NERD dataset from

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 and

Supported Tasks and Leaderboards



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.

    '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

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

More Information Needed

Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

CC BY-SA 4.0 license

Citation Information

    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 = "",
    doi = "10.18653/v1/2021.acl-long.248",
    pages = "3198--3213",


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