---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- am
- bbj
- bm
- ee
- ha
- ig
- lg
- luo
- mos
- ny
- pcm
- rw
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MasakhaNER-X
dataset_info:
- config_name: am
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 250
  - name: test
    num_examples: 500
- config_name: bbj
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 483
  - name: test
    num_examples: 966
- config_name: bm
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 638
  - name: test
    num_examples: 1000
- config_name: ee
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 501
  - name: test
    num_examples: 1000
- config_name: ha
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 272
  - name: test
    num_examples: 545
- config_name: ig
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 1000
  - name: test
    num_examples: 1000
- config_name: lg
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 906
  - name: test
    num_examples: 1000
- config_name: luo
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 644
  - name: validation
    num_examples: 92
  - name: test
    num_examples: 185
- config_name: mos
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 648
  - name: test
    num_examples: 1000
- config_name: ny
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 893
  - name: test
    num_examples: 1000
- config_name: pcm
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 1000
  - name: test
    num_examples: 1000
- config_name: rw
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 1000
  - name: test
    num_examples: 1000
- config_name: sn
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 887
  - name: test
    num_examples: 1000
- config_name: sw
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 1000
  - name: test
    num_examples: 1000
- config_name: tn
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 499
  - name: test
    num_examples: 996
- config_name: tw
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 605
  - name: test
    num_examples: 1000
- config_name: wo
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 923
  - name: test
    num_examples: 1000
- config_name: xh
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 817
  - name: test
    num_examples: 1000
- config_name: yo
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 1000
  - name: test
    num_examples: 1000
- config_name: zu
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: spans
    sequence: string
  - name: target
    dtype: string
  splits:
  - name: train
    num_examples: 1441
  - name: validation
    num_examples: 836
  - name: test
    num_examples: 1000
config_names:
- am
- bbj
- bm
- ee
- ha
- ig
- lg
- luo
- mos
- ny
- pcm
- rw
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
---

# Dataset Card for MasakhaNER

## 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:** [homepage](https://github.com/masakhane-io/masakhane-ner/tree/main/xtreme-up/MasakhaNER-X)
- **Repository:** [github](https://github.com/masakhane-io/masakhane-ner/tree/main/xtreme-up/MasakhaNER-X)
- **Paper:** [paper](https://aclanthology.org/2022.emnlp-main.298)
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de

### Dataset Summary

MasakhaNER-X is an aggregation of MasakhaNER 1.0 and MasakhaNER 2.0 datasets for 20 African languages. The dataset is not in CoNLL format. The input is the original raw text while the output is byte-level span annotations.

Example:
{"example_id": "test-00015916", "language": "pcm", "text": "By Bashir Ibrahim Hassan", "spans": [{"start_byte": 3, "limit_byte": 24, "label": "PER"}], "target": "PER: Bashir Ibrahim Hassan"}

MasakhaNER-X is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for twenty African languages:
- Amharic
- Ghomala
- Bambara
- Ewe
- Hausa
- Igbo
- Kinyarwanda
- Luganda
- Luo
- Mossi
- Chichewa
- Nigerian-Pidgin
- chiShona
- Swahili
- Setswana
- Twi
- Wolof
- Xhosa
- Yoruba
- Zulu

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

For more details see https://aclanthology.org/2022.emnlp-main.298


### Supported Tasks and Leaderboards

[More Information Needed]

- `named-entity-recognition`: The performance in this task is measured with [Span F1](https://github.com/google-research/multilingual-t5/blob/9dcd60fc43c31a8651461f9a21894a134ba22166/multilingual_t5/evaluation/metrics.py#L123) (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 twenty languages available :
- Amharic (am)
- Ghomala (bbj)
- Bambara (bm)
- Ewe (ee)
- Hausa (ha)
- Igbo (ig)
- Kinyarwanda (rw)
- Luganda (lg)
- Luo (luo)
- Mossi (mos)
- Chichewa (ny)
- Nigerian-Pidgin (pcm)
- chiShona (sn)
- Swahili (sw)
- Setswana (tn)
- Twi (tw)
- Wolof (wo)
- Xhosa (xh)
- Yoruba (yo)
- Zulu (zu)

## Dataset Structure

### Data Instances

The examples look like this for Nigerian-Pidgin:

```
from datasets import load_dataset
data = load_dataset('masakhaner-x', 'pcm') 

# Please, specify the language code

# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. 
{'id': '0',
 'text': "Most of de people who dey opposed to Prez Akufo-Addo en decision say within 3 weeks of lockdown, total number of cases for Ghana rise from around 100 catch 1024.",
 'spans': [{"start_byte": 42, "limit_byte": 52, "label": "PER"}, {"start_byte": 76, "limit_byte": 83, "label": "DATE"}, {"start_byte": 123, "limit_byte": 128, "label": "LOC"}]
 'target': "PER: Akufo-Addo $$ DATE: 3 weeks $$ LOC: Ghana"
}

```

### Data Fields

- `id`: id of the sample
- `text`: sentence containing entities
- `spans`: details of each named entities in the sentence
- `target`: named entities and their values. Each named entity is separated by '$$'

The NER tags correspond to this list:
```
"PER", "ORG", "LOC", and "DATE",
```

### Data Splits

For all languages, there are three splits - `train`, `validation` and `test` splits.

The splits have the following sizes :

| Language        | train | validation | test |
|-----------------|------:|-----------:|-----:|
| Amharic         |  1441 |        250 |  500 |
| Gbomola         |  1441 |        483 |  966 |
| Bambara         |  1441 |        638 |  1000|
| Ewe             |  1441 |        501 |  1000|
| Hausa           |  1441 |        1000|  1000|
| Igbo            |  1441 |        319 |  638 |
| Kinyarwanda     |  1441 |        1000|  1000|
| Luganda         |  1441 |        906 |  1000|
| Luo             |  644  |         92 |  185 |
| Mossi           |  1441 |        648 |  1000|
| Chichewa        |  1441 |        893 |  1000|
| Nigerian-Pidgin |  1441 |        1000|  1000|
| Shona           |  1441 |        887 |  1000|
| Swahili         |  1441 |        1000|  1000|
| Setswana        |  1441 |        499 |  996 |
| Twi             |  1441 |        605 |  1000|
| Wolof           |  1441 |        923 |  1000|
| Xhosa           |  1441 |        817 |  1000|
| Yoruba          |  1441 |        1000|  1000|
| Zulu            |  1441 |        836 |  1000|


### Licensing Information

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

### Citation Information

Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{adelani-etal-2022-masakhaner,
    title = "{M}asakha{NER} 2.0: {A}frica-centric Transfer Learning for Named Entity Recognition",
    author = "Adelani, David  and
      Neubig, Graham  and
      Ruder, Sebastian  and
      Rijhwani, Shruti  and
      Beukman, Michael  and
      Palen-Michel, Chester  and
      Lignos, Constantine  and
      Alabi, Jesujoba  and
      Muhammad, Shamsuddeen  and
      Nabende, Peter  and
      Dione, Cheikh M. Bamba  and
      Bukula, Andiswa  and
      Mabuya, Rooweither  and
      Dossou, Bonaventure F. P.  and
      Sibanda, Blessing  and
      Buzaaba, Happy  and
      Mukiibi, Jonathan  and
      Kalipe, Godson  and
      Mbaye, Derguene  and
      Taylor, Amelia  and
      Kabore, Fatoumata  and
      Emezue, Chris Chinenye  and
      Aremu, Anuoluwapo  and
      Ogayo, Perez  and
      Gitau, Catherine  and
      Munkoh-Buabeng, Edwin  and
      Memdjokam Koagne, Victoire  and
      Tapo, Allahsera Auguste  and
      Macucwa, Tebogo  and
      Marivate, Vukosi  and
      Elvis, Mboning Tchiaze  and
      Gwadabe, Tajuddeen  and
      Adewumi, Tosin  and
      Ahia, Orevaoghene  and
      Nakatumba-Nabende, Joyce  and
      Mokono, Neo Lerato  and
      Ezeani, Ignatius  and
      Chukwuneke, Chiamaka  and
      Oluwaseun Adeyemi, Mofetoluwa  and
      Hacheme, Gilles Quentin  and
      Abdulmumin, Idris  and
      Ogundepo, Odunayo  and
      Yousuf, Oreen  and
      Moteu, Tatiana  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.298",
    pages = "4488--4508",
    abstract = "African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14{\%} over 20 languages as compared to using English.",
}
```