README.md

---
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: source_project
    dtype: string
  splits:
  - name: train
    num_bytes: 1667483
    num_examples: 5700
  - name: test
    num_bytes: 288196
    num_examples: 964
  download_size: 607464
  dataset_size: 1955679
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: other
license_name: noodl-1.0
license_link: https://licensingafricandatasets.com/nwulite-obodo-license
language:
  - ln
tags:
  - LLM
  - lingala
  - Llama
pretty_name: Lingala Alpaca LLM Dataset
size_categories:
  - 1K<n<10K
---
# Lingala Alpaca LLM Dataset

This dataset contains human-generated instruction-following data in Lingala, inspired by the Alpaca dataset format, covering five task categories (formal, pedagogical, summary, urban, translation) across five public-interest domains, with a balanced representation of stylistic categories and instruction types.

## General Statistics
* **Total volume:** 6,664 lines in total.
* **Number of unique source texts:** 1,772.
* **Structure:** The dataset is divided into 5 instruction types, with a total of 6,100 context occurrences. This differs from the final number of lines because some contexts are intentionally reused across different tasks.

## Distribution by Instruction Type
The dataset is organized into five instruction categories:
* **Formal (`tasks_frm.json`):** 1,425 occurrences, covering administrative and standard responses.
* **Pedagogical (`tasks_pdg.json`):** 1,425 occurrences, covering explanation and teaching tasks.
* **Summary (`tasks_rsm.json`):** 1,425 occurrences, covering concise summarization tasks.
* **Urban (`tasks_urb.json`):** 1,425 occurrences, covering everyday and conversational language.
* **Translation (`tasks_trd.json`):** 400 occurrences, covering translation of source texts.

## Distribution by Domain and Complexity
### Domains Covered
The dataset covers the following domains:
* Agriculture
* Governance / Administration
* Health
* Informal Sector / Urban
* Education
Each domain contains 1,220 context occurrences.

### Complexity Levels
* **Basic:** 2,586 contexts (42.4%).
* **Intermediate:** 1,952 contexts (32.0%).
* **Complex:** 1,562 contexts (25.6%).

## Dataset Structure
### Splits
The final published dataset is divided into two splits:

| Split     |     Lines |
| --------- | --------: |
| Train     |     5,700 |
| Test      |       964 |
| **Total** | **6,664** |

### Fields
Each example contains the following fields:
* `instruction`
* `input`
* `output`
* `source_project`

## Dataset Creation

**Source Data**: instructions and responses written by the CDS team and institutional collaborators (UNDP, partner universities), based on 1,772 unique source texts after cross-batch deduplication.


## Format and Purpose
The dataset is published in an instruction-following format compatible with the Hugging Face `datasets` library.
The data is intended for training and fine-tuning AI language models to generate responses in Lingala adapted to different linguistic registers and task types.
The dataset covers:
* Formal communication
* Pedagogical explanations
* Everyday and urban conversation
* Text summarization
* Translation
It also provides coverage across several public-interest domains, including agriculture, governance and administration, health, education, and the informal/urban sector.

## Considerations for Using the Data
This dataset intentionally covers 5 broad domains (agriculture, governance, health, informal sector, education) but remains modest in size (6,664 examples) for a low-resource language — users should expect limited lexical and stylistic coverage outside these domains.

## Usage
To load this dataset:
```python
from datasets import load_dataset
dataset = load_dataset("Congo-digital-service/Dataset-Lingala-Alpaca-LLM")
print(dataset)
```

## Language
* Lingala (`ln`)

## License
This dataset is published under the **Nwulite Obodo Open Data License (NOODL-1.0)**, a license designed for the equitable sharing of African datasets.

> This dataset, Dataset-Lingala-Alpaca-LLM (Congo-digital-service/Dataset-Lingala-Alpaca-LLM), was created by Congo Digital Services (CDS SARL) (https://www.congo-digital.com/) in August 2026 in collaboration with the MALOBA community (https://maloba.congo-digital.com/), Radio Rurale, the Service National des Grandes Endémies de Brazzaville, the Faculté des Lettres et des Langues Vivantes, Université Marien Ngouabi (UMNG), the Ministry of Posts, Telecommunications and Digital Economy of the Republic of Congo, and UNDP Congo. It is licensed under the Nwulite Obodo Open Data License (https://licensingafricandatasets.com/nwulite-obodo-license).
>
> In exchange for use of this dataset, users from developing countries are required to provide: Use of the dataset (no additional benefit required). Users from high-income countries or commercial entities are additionally required to publicly acknowledge and credit the Maloba Project (UNDP Republic of Congo — language digitalisation initiative) in any publication, product, model, or output derived from this dataset. To fulfil this requirement, contact [contact@congo-digital.com](mailto:contact@congo-digital.com) or visit https://www.congo-digital.com/contact.


### Creators
- **Congo Digital Services (CDS SARL)** — https://www.congo-digital.com/
- In collaboration with:
  - the **MALOBA community** — https://maloba.congo-digital.com/
  - **SIL Congo**
  - the **Faculté des Lettres et des Langues Vivantes, Université Marien Ngouabi (UMNG)**
  - the **Ministry of Posts, Telecommunications and Digital Economy of the Republic of Congo**
  - **UNDP Congo**
- Created in August 2026