
# Dataset Card for AfriQA

## 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 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/afriqa)
- **Repository:** [github](https://github.com/masakhane-io/afriqa)
- **Paper:** [paper]()
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or oogundep@uwaterloo.ca

### Dataset Summary

AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.

The train/validation/test sets are available for all the 10 languages.


### Supported Tasks and Leaderboards

- `question-answering`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better) and [Exact Match Accuracy](https://huggingface.co/spaces/evaluate-metric/exact_match).

### Languages

There are 20 languages available :
- Bemba (bem)
- Fon (fon)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Swahili (swą)
- Twi (twi)
- Wolof (wol)
- Yorùbá (yor)
- Zulu (zul)

## Dataset Structure

### Data Instances

- Data Format:
- id : Question ID
- question : Question in African Language
- translated_question : Question translated into a pivot language (English/French)
- answers : Answer in African Language
- lang : Datapoint Language (African Language) e.g `bem`
- split : Dataset Split
- translated_answer : Answer in Pivot Language
- translation_type : Translation type of question and answers


```bash
{   "id": 0, 
    "question": "Bushe icaalo ca Egypt caali tekwapo ne caalo cimbi?", 
    "translated_question": "Has the country of Egypt been colonized before?", 
    "answers": "['Emukwai']", 
    "lang": "bem", 
    "split": "dev", 
    "translated_answer": "['yes']", 
    "translation_type": "human_translation"
    }
```

### 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 | dev | test |
|-----------------|------:|-----------:|-----:|
| Bemba         |  502 | 503 |  314 |
| Fon          |  427 | 428 |  386 |
| Hausa           |  435 | 436 |  300 |
| Igbo            |  417 | 418 |  409 |
| Kinyarwanda     	  |   407 |  409 |  347 |
| Swahili       |  415 |   417 |  302 |
| Twi          |  451 |   452 |  490 |
| Wolof        |  503 |    504 |  334 |
| Yoruba          |  360 |   361 |  332 |
| Zulu        |  387 |    388 |  325 |
| <b>Total</b>    |  <b>4333</b>  |  <b>4346</b>  |<b>3560</b>  |

## Dataset Creation

### Curation Rationale

The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing.

[More Information Needed]

### Source Data

...

#### Initial Data Collection and Normalization

...

#### Who are the source language producers?

...

### Annotations

#### Annotation process

Details can be found here ...

#### Who are the annotators?

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

### Personal and Sensitive Information

...

## 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:

```
@misc{ogundepo2023afriqa,
      title={AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages}, 
      author={Odunayo Ogundepo and Tajuddeen R. Gwadabe and Clara E. Rivera and Jonathan H. Clark and Sebastian Ruder and David Ifeoluwa Adelani and Bonaventure F. P. Dossou and Abdou Aziz DIOP and Claytone Sikasote and Gilles Hacheme and Happy Buzaaba and Ignatius Ezeani and Rooweither Mabuya and Salomey Osei and Chris Emezue and Albert Njoroge Kahira and Shamsuddeen H. Muhammad and Akintunde Oladipo and Abraham Toluwase Owodunni and Atnafu Lambebo Tonja and Iyanuoluwa Shode and Akari Asai and Tunde Oluwaseyi Ajayi and Clemencia Siro and Steven Arthur and Mofetoluwa Adeyemi and Orevaoghene Ahia and Aremu Anuoluwapo and Oyinkansola Awosan and Chiamaka Chukwuneke and Bernard Opoku and Awokoya Ayodele and Verrah Otiende and Christine Mwase and Boyd Sinkala and Andre Niyongabo Rubungo and Daniel A. Ajisafe and Emeka Felix Onwuegbuzia and Habib Mbow and Emile Niyomutabazi and Eunice Mukonde and Falalu Ibrahim Lawan and Ibrahim Said Ahmad and Jesujoba O. Alabi and Martin Namukombo and Mbonu Chinedu and Mofya Phiri and Neo Putini and Ndumiso Mngoma and Priscilla A. Amuok and Ruqayya Nasir Iro and Sonia Adhiambo},
      year={2023},
      eprint={2305.06897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

### Contributions

Thanks to [@ToluClassics](https://github.com/ToluClassics) for adding this dataset.
task_categories:question-answering multilinguality:multilingual size_categories:10K<n<100K language:bem language:fon language:ha language:ig language:kin language:sw language:wo language:yo language:zu language:tw license:cc-by-sa-4.0 cross-lingual question-answering qa arxiv:2305.06897 region:us
{"language": ["bem", "fon", "ha", "ig", "kin", "sw", "wo", "yo", "zu", "tw"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "AfriQA", "tags": ["cross-lingual", "question-answering", "qa"]}
TAGS
#task_categories-question-answering #multilinguality-multilingual #size_categories-10K<n<100K #language-Bemba (Zambia) #language-Fon #language-Hausa #language-Igbo #language-Kinyarwanda #language-Swahili (macrolanguage) #language-Wolof #language-Yoruba #language-Zulu #language-Twi #license-cc-by-sa-4.0 #cross-lingual #question-answering #qa #arxiv-2305.06897 #region-us 

Dataset Card for AfriQA
=======================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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


Dataset Description
-------------------


* Homepage: homepage
* Repository: github
* Paper: paper
* Point of Contact: Masakhane or oogundep@URL


### Dataset Summary


AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.


The train/validation/test sets are available for all the 10 languages.


### Supported Tasks and Leaderboards


* 'question-answering': The performance in this task is measured with F1 (higher is better) and Exact Match Accuracy.


### Languages


There are 20 languages available :


* Bemba (bem)
* Fon (fon)
* Hausa (hau)
* Igbo (ibo)
* Kinyarwanda (kin)
* Swahili (swą)
* Twi (twi)
* Wolof (wol)
* Yorùbá (yor)
* Zulu (zul)


Dataset Structure
-----------------


### Data Instances


* Data Format:
* id : Question ID
* question : Question in African Language
* translated\_question : Question translated into a pivot language (English/French)
* answers : Answer in African Language
* lang : Datapoint Language (African Language) e.g 'bem'
* split : Dataset Split
* translated\_answer : Answer in Pivot Language
* translation\_type : Translation type of question and answers


### 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 :



Dataset Creation
----------------


### Curation Rationale


The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing.


### Source Data


...


#### Initial Data Collection and Normalization


...


#### Who are the source language producers?


...


### Annotations


#### Annotation process


Details can be found here ...


#### Who are the annotators?


Annotators were recruited from Masakhane


### Personal and Sensitive Information


...


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### 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


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


### Contributions


Thanks to @ToluClassics for adding this dataset.



### Dataset Summary


AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.


The train/validation/test sets are available for all the 10 languages. ### Supported Tasks and Leaderboards


* 'question-answering': The performance in this task is measured with F1 (higher is better) and Exact Match Accuracy. ### Languages


There are 20 languages available :


* Bemba (bem)
* Fon (fon)
* Hausa (hau)
* Igbo (ibo)
* Kinyarwanda (kin)
* Swahili (swą)
* Twi (twi)
* Wolof (wol)
* Yorùbá (yor)
* Zulu (zul)


Dataset Structure
----------------- ### Data Instances


* Data Format:
* id : Question ID
* question : Question in African Language
* translated\_question : Question translated into a pivot language (English/French)
* answers : Answer in African Language
* lang : Datapoint Language (African Language) e.g 'bem'
* split : Dataset Split
* translated\_answer : Answer in Pivot Language
* translation\_type : Translation type of question and answers ### 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 :



Dataset Creation
---------------- ### Curation Rationale


The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing. ### Source Data


... #### Initial Data Collection and Normalization


... #### Who are the source language producers?


... ### Annotations #### Annotation process


Details can be found here ... #### Who are the annotators?


Annotators were recruited from Masakhane ### Personal and Sensitive Information


...


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### 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


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


Thanks to @ToluClassics for adding this dataset.
TAGS
#task_categories-question-answering #multilinguality-multilingual #size_categories-10K<n<100K #language-Bemba (Zambia) #language-Fon #language-Hausa #language-Igbo #language-Kinyarwanda #language-Swahili (macrolanguage) #language-Wolof #language-Yoruba #language-Zulu #language-Twi #license-cc-by-sa-4.0 #cross-lingual #question-answering #qa #arxiv-2305.06897 #region-us 
 ### Dataset Summary


AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.


The train/validation/test sets are available for all the 10 languages. ### Supported Tasks and Leaderboards


* 'question-answering': The performance in this task is measured with F1 (higher is better) and Exact Match Accuracy. ### Languages


There are 20 languages available :


* Bemba (bem)
* Fon (fon)
* Hausa (hau)
* Igbo (ibo)
* Kinyarwanda (kin)
* Swahili (swą)
* Twi (twi)
* Wolof (wol)
* Yorùbá (yor)
* Zulu (zul)


Dataset Structure
----------------- ### Data Instances


* Data Format:
* id : Question ID
* question : Question in African Language
* translated\_question : Question translated into a pivot language (English/French)
* answers : Answer in African Language
* lang : Datapoint Language (African Language) e.g 'bem'
* split : Dataset Split
* translated\_answer : Answer in Pivot Language
* translation\_type : Translation type of question and answers ### 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 :



Dataset Creation
---------------- ### Curation Rationale


The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing. ### Source Data


... #### Initial Data Collection and Normalization


... #### Who are the source language producers?


... ### Annotations #### Annotation process


Details can be found here ... #### Who are the annotators?


Annotators were recruited from Masakhane ### Personal and Sensitive Information


...


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### 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


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


Thanks to @ToluClassics for adding this dataset.
passage: TAGS
#task_categories-question-answering #multilinguality-multilingual #size_categories-10K<n<100K #language-Bemba (Zambia) #language-Fon #language-Hausa #language-Igbo #language-Kinyarwanda #language-Swahili (macrolanguage) #language-Wolof #language-Yoruba #language-Zulu #language-Twi #license-cc-by-sa-4.0 #cross-lingual #question-answering #qa #arxiv-2305.06897 #region-us 
### Dataset Summary


AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.


The train/validation/test sets are available for all the 10 languages.### Supported Tasks and Leaderboards


* 'question-answering': The performance in this task is measured with F1 (higher is better) and Exact Match Accuracy.### Languages


There are 20 languages available :


* Bemba (bem)
* Fon (fon)
* Hausa (hau)
* Igbo (ibo)
* Kinyarwanda (kin)
* Swahili (swą)
* Twi (twi)
* Wolof (wol)
* Yorùbá (yor)
* Zulu (zul)


Dataset Structure
-----------------### Data Instances


* Data Format:
* id : Question ID
* question : Question in African Language
* translated\_question : Question translated into a pivot language (English/French)
* answers : Answer in African Language
* lang : Datapoint Language (African Language) e.g 'bem'
* split : Dataset Split
* translated\_answer : Answer in Pivot Language
* translation\_type : Translation type of question and answers