---
language:
- sw
- xh
- zu
- ha
- yo
size_categories:
- 100M<n<1B
---

# Inkuba-Instruct dataset

### Dataset Description

- **Collected by:** [Lelapa AI](https://lelapa.ai/) - Fundamental Research Team
- **Shared by:** [Lelapa AI](https://lelapa.ai/)
- **Language(s) (NLP):** isiZulu, Yoruba, Swahili, isiXhosa and Hausa
- **License:** CC BY-NC 4.0

### Dataset Sources

- We collected downstream datasets for 5 African languages from the following public repositories:

|           Task            |                Datasets              |                       Sources                        | Combined Size (# of samples) |
|---------------------------|--------------------------------------|------------------------------------------------------|------------------------------|
| Machine Translation       | WMT-22-African, Mafand-MT, Menyo-20k | allenai/wmt22_african, masakhane/mafand, menyo20k_mt | 359 M |
| NER                       | MasakhaNER2, Hausa VoA NER, isiXhosa NER Corpus | masakhane/masakhaner2, hausa_voa_ner, https://repo.sadilar.org/handle/20.500.12185/312 | ~64k |
| POS                       | MasakhaPOS                                      | masakhane/masakhapos                      | 6.5k |
| Question-Answering        |                afriqa                | masakhane/afriqa                                     | 4.45k |
| Topic Classification      | SIB-200, MasakhaNEWS, Hausa News Classification | Davlan/sib200,masakhane/masakhanews, hausa_voa_topics | 22.8k |
| Sentiment Analysis        | AfriSenti, NaijaSenti, Swhaili-Tweet-Sentiment  | shmuhammad/AfriSenti-twitter-sentiment,HausaNLP/NaijaSenti-Twitter,Davis/Swahili-tweet-sentiment | 46.62k |

- **Paper:** [InkubaLM](https://arxiv.org/pdf/2408.17024) 

#### Dataset statistics

| Language     | Number of samples |
|--------------|-------------------|
| Hausa        | 5.8 M             |
| Yoruba       | 6.4 M             |
| Swahili      | 62.41 M           |
| isiZulu      | 16.20 M           |
| isiXhosa     | 25.35 M           |
| English **   | 95.42 M           |

** Only for Machine Translation `eng-xxx` & `xxx-eng`

## Uses
- This dataset can be utilized to instruct finetune language models. Only the train and dev set are currently open sourced. The test set will be made open-source at a later date.

### How to Use

``` python 
from datasets import load_dataset
data = load_dataset("lelapa/lelapa_instruct_datasets_no_test_set", "swahili_train,)
``` 

### Structure
```
{
	"instruction": "Ainisha mada ya ...",
	"input": "Je chanjo ya corona ...",
	"output": "afya",
	"data_source": "masakhanews",
	"task": "news_classification",
}
```

Datasets `task` keys are as follows:

```
{
	"mmt": machine translate,
	"ner": maned entity recognition,
	"pos": part-of-speech tagging,
	"sentiment": sentiment analysis,
	"news_classification": topic classification
}

```
The keyword `news_classification` is used for the `topic classification` task because we pulled together the data from both topic classification and news classification datasets. We also considered/casted the latter as a topic classification problem.

## Citation

```
@article{tonja2024inkubalm,
  title={InkubaLM: A small language model for low-resource African languages},
  author={Tonja, Atnafu Lambebo and Dossou, Bonaventure FP and Ojo, Jessica and Rajab, Jenalea and Thior, Fadel and Wairagala, Eric Peter and Anuoluwapo, Aremu and Moiloa, Pelonomi and Abbott, Jade and Marivate, Vukosi and others},
  journal={arXiv preprint arXiv:2408.17024},
  year={2024}
}
```

## Dataset Card Authors 

[Lelapa AI](https://lelapa.ai/) - Fundamental Research Team

## Dataset Card Contact

[Lelapa AI](https://lelapa.ai/)