Datasets:
metadata
license: cc-by-4.0
task_categories:
- token-classification
language:
- bn
- de
- en
- es
- fa
- hi
- ko
- nl
- ru
- tr
- zh
- multilingual
tags:
- multiconer
- ner
- multilingual
- named entity recognition
size_categories:
- 100K<n<1M
dataset_info:
- config_name: bn
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
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- config_name: de
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
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- config_name: en
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
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- name: validation
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- config_name: es
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
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- name: validation
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- config_name: fa
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
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'11': B-CW
'12': I-CW
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- config_name: hi
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
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'3': B-LOC
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'5': B-CORP
'6': I-CORP
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- config_name: ko
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
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'5': B-CORP
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- name: test
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dataset_size: 32202854
- config_name: mix
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
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'5': B-CORP
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'7': B-GRP
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'11': B-CW
'12': I-CW
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dataset_size: 20627302
- config_name: multi
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
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- config_name: nl
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
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- config_name: ru
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
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'5': B-CORP
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dataset_size: 53052185
- config_name: tr
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
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dataset_size: 19069637
- config_name: zh
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
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'1': B-PER
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'3': B-LOC
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'5': B-CORP
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dataset_size: 35559142
Multilingual Complex Named Entity Recognition (MultiCoNER)
Dataset Summary
MultiCoNER (version 1) is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation.
See the AWS Open Data Registry entry for MultiCoNER for more information.
Labels
PER: Person, i.e. names of peopleLOC: Location, i.e. locations/physical facilitiesCORP: Corporation, i.e. corporations/businessesGRP: Groups, i.e. all other groupsPROD: Product, i.e. consumer productsCW: Creative Work, i.e. movies/songs/book titles
Dataset Structure
The dataset follows the IOB format of CoNLL. In particular, it uses the following label to ID mapping:
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-CORP": 5,
"I-CORP": 6,
"B-GRP": 7,
"I-GRP": 8,
"B-PROD": 9,
"I-PROD": 10,
"B-CW": 11,
"I-CW": 12,
}
Languages
The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese.
Usage
from datasets import load_dataset
dataset = load_dataset('tomaarsen/MultiCoNER', 'multi')
License
CC BY 4.0
Citation
@misc{malmasi2022multiconer,
title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition},
author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
year={2022},
eprint={2208.14536},
archivePrefix={arXiv},
primaryClass={cs.CL}
}