Datasets:
File size: 2,914 Bytes
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---
dataset_info:
features:
- name: id
dtype: int64
- name: image_id
dtype: string
- name: eng
dtype: string
- name: afr
dtype: string
- name: amh
dtype: string
- name: bem
dtype: string
- name: cjk
dtype: string
- name: dik
dtype: string
- name: dyu
dtype: string
- name: ewe
dtype: string
- name: fuv
dtype: string
- name: hau
dtype: string
- name: ibo
dtype: string
- name: kik
dtype: string
- name: kab
dtype: string
- name: kam
dtype: string
- name: kon
dtype: string
- name: kmb
dtype: string
- name: lua
dtype: string
- name: lug
dtype: string
- name: lin
dtype: string
- name: kin
dtype: string
- name: yor
dtype: string
splits:
- name: train
num_bytes: 12340971
num_examples: 8091
download_size: 5936673
dataset_size: 12340971
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- translation
---
## AfriMMD - African Multilingual Multimodal Dataset (POC)
AfriMMD is a multilingual dataset created to enhance linguistic diversity in AI,
focusing on African languages. This is a proof-of-concept experiment on the use
of multimodal datasets to represent African languages in AI. The dataset contains
translations of the captions in the widely-used Flickr8k dataset into 20 African
languages. The goal is to address the underrepresentation of African languages
in AI and foster more inclusive AI technologies. The image-text pairs have been
carefully translated into multiple African languages, providing an avenue
for advanced and inclusive AI development, particularly in multimodal tasks that
involve both text and images.
Images associated with the dataset can manually be downloaded from [Github](https://github.com/jbrownlee/Datasets/releases/tag/Flickr8k)
or [Kaggle](https://www.kaggle.com/datasets/adityajn105/flickr8k?select=Images)
## Supported Languages
Amharic (amh), Bemba (bem), Chokwe (cjk), Rek (dik), Dyula (dyu), Ewe (ewe),
Fulfulde (fuv), Hausa (hau), Igbo (ibo), Kikuyu (kik), Kabyle (kab),
Kamba (kam), Kikongo (kon), Kimbundu (kmb), LubaKasai (lua), Ganda (lug),
Lingala (lin), Kinyarwanda (kin), Yoruba (yor)
## Load Dataset
```python
from datasets import load_dataset
dataset = load_dataset('AfriMM/AfriMMD')
```
## Applications
- Multilingual multimodal tasks (eg: image captioning in African languages, pre-trained vision-language models, etc.)
- Translation and language learning for supported African languages.
- Research on cross-cultural understanding and representation in AI.
## Citation
```bibtex
@dataset{afrimm2024,
author = {AfriMM - ML Collective},
title = {AfriMMD: Multimodal Dataset for African Languages},
year = 2024,
url = {https://huggingface.co/datasets/AfriMM/AfriMMD}
}
``` |