README.md

---
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}
}
```