metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - am
  - ha
  - ig
  - lg
  - luo
  - pcm
  - rw
  - sw
  - wo
  - yo
license:
  - unknown
multilinguality:
  - multilingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
paperswithcode_id: null
pretty_name: MasakhaNER
configs:
  - am
  - ha
  - ig
  - lg
  - luo
  - pcm
  - rw
  - sw
  - wo
  - yo
dataset_info:
  - config_name: amh
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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        num_examples: 1750
      - name: validation
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      - name: test
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    download_size: 571951
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  - config_name: hau
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
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      - name: validation
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  - config_name: ibo
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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      - name: validation
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      - name: test
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    download_size: 515415
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  - config_name: kin
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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      - name: validation
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      - name: test
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    download_size: 633024
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  - config_name: lug
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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      - name: validation
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      - name: test
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    download_size: 445755
    dataset_size: 865038
  - config_name: luo
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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      - name: validation
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        num_examples: 92
      - name: test
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    download_size: 213281
    dataset_size: 446217
  - config_name: pcm
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
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      - name: validation
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        num_examples: 306
      - name: test
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        num_examples: 600
    download_size: 572054
    dataset_size: 1257243
  - config_name: swa
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
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        num_examples: 2109
      - name: validation
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        num_examples: 300
      - name: test
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        num_examples: 604
    download_size: 686313
    dataset_size: 1401791
  - config_name: wol
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
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        num_examples: 1871
      - name: validation
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        num_examples: 267
      - name: test
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        num_examples: 539
    download_size: 364463
    dataset_size: 865095
  - config_name: yor
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
              '7': B-DATE
              '8': I-DATE
    splits:
      - name: train
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        num_examples: 2171
      - name: validation
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        num_examples: 305
      - name: test
        num_bytes: 359519
        num_examples: 645
    download_size: 751510
    dataset_size: 1503675

Dataset Card for MasakhaNER

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: homepage
Repository: github
Paper: paper
Point of Contact: Masakhane or [email protected]

Dataset Summary

MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages.

Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:

[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .

MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for ten African languages:

Amharic
Hausa
Igbo
Kinyarwanda
Luganda
Luo
Nigerian-Pidgin
Swahili
Wolof
Yoruba

The train/validation/test sets are available for all the ten languages.

For more details see https://arxiv.org/abs/2103.11811

Supported Tasks and Leaderboards

[More Information Needed]

named-entity-recognition: The performance in this task is measured with F1 (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.

Languages

There are ten languages available :

Amharic (amh)
Hausa (hau)
Igbo (ibo)
Kinyarwanda (kin)
Luganda (kin)
Luo (luo)
Nigerian-Pidgin (pcm)
Swahili (swa)
Wolof (wol)
Yoruba (yor)

Dataset Structure

Data Instances

The examples look like this for Yorùbá:

from datasets import load_dataset
data = load_dataset('masakhaner', 'yor') 

# Please, specify the language code

# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
{'id': '0',
 'ner_tags': [B-DATE, I-DATE, 0, 0, 0, 0, 0, B-PER, I-PER, I-PER, O, O, O, O],
 'tokens': ['Wákàtí', 'méje', 'ti', 'ré', 'kọjá', 'lọ', 'tí', 'Luis', 'Carlos', 'Díaz', 'ti', 'di', 'awati', '.']
}

Data Fields

id: id of the sample
tokens: the tokens of the example text
ner_tags: the NER tags of each token

The NER tags correspond to this list:

"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",

In the NER tags, a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & time (DATE).

It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.

Data Splits

For all languages, there are three splits.

The original splits were named train, dev and test and they correspond to the train, validation and test splits.

The splits have the following sizes :

Language	train	validation	test
Amharic	1750	250	500
Hausa	1903	272	545
Igbo	2233	319	638
Kinyarwanda	2110	301	604
Luganda	2003	200	401
Luo	644	92	185
Nigerian-Pidgin	2100	300	600
Swahili	2104	300	602
Wolof	1871	267	536
Yoruba	2124	303	608

Dataset Creation

Curation Rationale

The dataset was introduced to introduce new resources to ten languages that were under-served for natural language processing.

[More Information Needed]

Source Data

The source of the data is from the news domain, details can be found here https://arxiv.org/abs/2103.11811

Initial Data Collection and Normalization

The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.

Who are the source language producers?

The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.

Annotations

Annotation process

Details can be found here https://arxiv.org/abs/2103.11811

Who are the annotators?

Annotators were recruited from Masakhane

Personal and Sensitive Information

The data is sourced from newspaper source and only contains mentions of public figures or individuals

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.

Additional Information

Dataset Curators

Licensing Information

The licensing status of the data is CC 4.0 Non-Commercial

Citation Information

Provide the BibTex-formatted reference for the dataset. For example:

@article{Adelani2021MasakhaNERNE,
  title={MasakhaNER: Named Entity Recognition for African Languages},
  author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos 
  and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and 
  Israel Abebe Azime and S. Muhammad and Chris C. Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and 
  Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and J. Alabi and Seid Muhie Yimam and Tajuddeen R. Gwadabe and
  Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and V. Otiende and Iroro Orife and Davis David and 
  Samba Ngom and Tosin P. Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and 
  C. Chukwuneke and N. Odu and Eric Peter Wairagala and S. Oyerinde and Clemencia Siro and Tobius Saul Bateesa and 
  Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and 
  Ayodele Awokoya and Mouhamadane Mboup and D. Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and
   Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and 
   Thierno Ibrahima Diop and A. Diallo and Adewale Akinfaderin and T. Marengereke and Salomey Osei},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.11811}
}

Contributions

Thanks to @dadelani for adding this dataset.

Datasets:

masakhane
/

masakhaner

Dataset Card for MasakhaNER

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

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

Contributions