README.md

---
annotations_creators:
- expert-generated
language:
- bm
- bbj
- ee
- fon
- ha
- ig
- rw
- lg
- luo
- mos
- ny
- pcm
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhapos
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- pos
- masakhapos
- masakhane
task_categories:
- token-classification
task_ids:
- named-entity-recognition

---


# Dataset Card for [Dataset Name]

## Table of Contents
- [Table of Contents](#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-pos/)
- **Repository:** [github](https://github.com/masakhane-io/masakhane-pos/)
- **Paper:** [paper](https://aclanthology.org/2023.acl-long.609/)
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de

### Dataset Summary

MasakhaPOS is the largest publicly available high-quality dataset for part-of-speech (POS) tagging in 20 African languages. The languages covered are: 


The train/validation/test sets are available for all the 20 languages.

For more details see https://aclanthology.org/2023.acl-long.609/


### Supported Tasks and Leaderboards

[More Information Needed]

- `Part-of-speech`: The performance in this task is measured with [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) (higher is better). 
### Languages

There are 20 languages available :
- Bambara (bam)
- Ghomala (bbj)
- Ewe (ewe)
- Fon (fon)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Luganda (lug)
- Dholuo (luo) 
- Mossi (mos)
- Chichewa (nya)
- Nigerian Pidgin
- chShona (sna)
- Kiswahili (swą)
- Setswana (tsn)
- Twi (twi)
- Wolof (wol)
- isiXhosa (xho)
- Yorùbá (yor)
- isiZulu (zul)

## Dataset Structure

### Data Instances

The examples look like this for Yorùbá:

```
from datasets import load_dataset
data = load_dataset('masakhane/masakhapos', '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':  [0, 10, 10, 16, 0, 14, 0, 16, 0],
 'tokens': ['Ọ̀gbẹ́ni', 'Nuhu', 'Adam', 'kúrò', 'nípò', 'bí', 'ẹní', 'yọ', 'jìgá']
}
```

### Data Fields

- `id`: id of the sample
- `tokens`: the tokens of the example text
- `upos`: the POS tags of each token

The POS tags correspond to this list:
```
"NOUN", "PUNCT", "ADP", "NUM", "SYM", "SCONJ", "ADJ", "PART", "DET", "CCONJ", "PROPN", "PRON", "X", "ADV", "INTJ", "VERB", "AUX",```
              
The definition  of the tags can be found on [UD website](https://universaldependencies.org/u/pos/)

### 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  |
|-----------------|------:|-----------:|------:|
| Bambara         |  775  |        154 |  619  |
| Ghomala         |  750  |        149 |  599  |
| Ewe             |  728  |        145 |  582  |
| Fon             |  810  |        161 |  646  |
| Hausa           |  753  |        150 |  601  |
| Igbo            |  803  |        160 |  642  |
| Kinyarwanda     |  757  |        151 |  604  |
| Luganda         |  733  |        146 |  586  |
| Luo             |  758  |        151 |  606  |
| Mossi           |  757  |        151 |  604  |
| Chichewa        |  728  |        145 |  582  |
| Nigerian-Pidgin |  752  |        150 |  600  |
| chiShona        |  747  |        149 |  596  |
| Kiswahili       |  693  |        138 |  553  |
| Setswana        |  754  |        150 |  602  |
| Akan/Twi        |  785  |        157 |  628  |
| Wolof           |  782  |        156 |  625  |
| isiXhosa        |  752  |        150 |  601  |
| Yoruba          |  893  |        178 |  713  |
| isiZulu         |  753  |        150 |  601  |

## Dataset Creation

### Curation Rationale

The dataset was introduced to introduce new resources to 20 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://aclanthology.org/2023.acl-long.609/
#### 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://aclanthology.org/2023.acl-long.609/

#### Who are the annotators?

Annotators were recruited from [Masakhane](https://www.masakhane.io/)

### 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](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{dione-etal-2023-masakhapos,
    title = "{M}asakha{POS}: Part-of-Speech Tagging for Typologically Diverse {A}frican languages",
    author = "Dione, Cheikh M. Bamba  and Adelani, David Ifeoluwa  and Nabende, Peter  and Alabi, Jesujoba  and Sindane, Thapelo  and Buzaaba, Happy  and Muhammad, Shamsuddeen Hassan  and Emezue, Chris Chinenye  and Ogayo, Perez  and Aremu, Anuoluwapo  and Gitau, Catherine  and Mbaye, Derguene  and Mukiibi, Jonathan  and Sibanda, Blessing  and Dossou, Bonaventure F. P.  and Bukula, Andiswa  and Mabuya, Rooweither  and Tapo, Allahsera Auguste  and Munkoh-Buabeng, Edwin  and Memdjokam Koagne, Victoire  and Ouoba Kabore, Fatoumata  and Taylor, Amelia  and Kalipe, Godson  and Macucwa, Tebogo  and Marivate, Vukosi  and Gwadabe, Tajuddeen  and Elvis, Mboning Tchiaze  and Onyenwe, Ikechukwu  and Atindogbe, Gratien  and Adelani, Tolulope  and Akinade, Idris  and Samuel, Olanrewaju  and Nahimana, Marien  and Musabeyezu, Th{\'e}og{\`e}ne  and Niyomutabazi, Emile  and Chimhenga, Ester  and Gotosa, Kudzai  and Mizha, Patrick  and Agbolo, Apelete  and Traore, Seydou  and Uchechukwu, Chinedu  and Yusuf, Aliyu  and Abdullahi, Muhammad  and Klakow, Dietrich",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.609",
    doi = "10.18653/v1/2023.acl-long.609",
    pages = "10883--10900",
    abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.",
}
```

### Contributions

Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.