updates

README.md

@@ -16,4 +16,190 @@ language:
 pretty_name: AfriQA
 size_categories:
 - 10K<n<100K
----
+multilinguality:
+- multilingual
+tags:
+- cross-lingual
+- question-answering
+- qa
+---
+
+# 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 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 [email protected]
+
+### 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.
+
+For more details see ...
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+- `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:
+
+```
+Incoming ...
+```
+
+### Contributions
+
+Thanks to [@ToluClassics](https://github.com/ToluClassics) for adding this dataset.