---
task_categories:
- text-generation
language:
- rw
size_categories:
- 1K<n<10K
---
### Dataset Summary

The Kinyarwanda Monolingual Dataset version 1 is a large collection of Kinyarwanda language texts aimed at supporting the development of NLP and AI applications which can process Kinyarwanda texts. This dataset contains 78k documents, totalling about 25 million words, and includes diverse content types such as news articles, government reports, religious texts, legal documents, educational materials, and cultural narratives. The dataset has been collected to address the lack of open-source language resources for African languages, with a specific focus on Kinyarwanda.

### Dataset Details

- **Languages**: Kinyarwanda
- **Size**: about 78,000 documents totalling 25 million words (for comparison : Kinyarwanda Bible has about 575k words, so the dataset is equivalent to over 40 bibles)
- **Collection Methodology**: The dataset was created using content available in HTML and PDF formats, covering diverse topics and content types. HTML sources included news sites (e.g., Kigali Today, Igihe), religious sites, Wikipedia, and cultural storytelling websites (e.g., Imigani.rw). PDF documents consisted of government reports, legal texts, transcripts of Senate debates, and educational materials, ensuring a wide representation of language use in different contexts.
- **Content**: The dataset represents a broad cross-section of Kinyarwanda language use, encompassing both formal and informal registers. The content spans news media, cultural stories, bureaucratic documents, legal records, religious teachings, and educational texts.

### Motivation and Background

This dataset aims to address the scarcity of language resources for African languages, particularly in the context of large language models (LLMs). Despite the significant advancements in generative AI, African languages have largely been excluded from these models, limiting their applicability in developing countries. By creating this dataset, we hope to bridge the gap and ensure that the benefits of AI technology can be equitably distributed. The Kinyarwanda Monolingual Dataset also supports Rwanda's national AI policy, which emphasizes data as a critical resource for AI development.


### Limitation and Future Work

This is, to our knowledge, the largest open monolingual dataset in Kinyarwanda. However, its size remains limited. We plan to expand the dataset by identifying additional content sources and addressing the current gaps in representation. We will also continuously improve the data quality, for example by improving text extraction tools.

**Licensing. Use Cases and Usage Limitations**

This dataset is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

The dataset is suitable for fine-tuning existing LLMs to better support African languages, addressing the current gap in language model performance for low-resource languages. It can also be used for other NLP tasks which require a large collection of monolingual texts in Kinyarwanda.

The copyright of the individual texts in this collection remains with their original authors. Reproducing substantial portions of this dataset for presentation to human readers (e.g., in websites, publications, or documents) may conflict with the interests of the copyright holders. Therefore, the use of this dataset should be restricted to data processing with computers for statistical analysis and information extraction, primarily aimed at training AI models to process Kinyarwanda texts—similar to how written materials are used for language learning.

### Citation

Mbaza NLP Community (2024). Kinyarwanda Monolingual Dataset. Version 1.0. <https://huggingface.co/datasets/mbazaNLP/kinyarwanda_monolingual_v01.0> 




```
dataset_info:
  features:
  - name: text
    dtype: string
  - name: nwords           ## number of words
    dtype: int64  
  - name: ntokens_llama32  ## number of tokens LLama 3.2 tokenizers 
    dtype: int64
  splits:
  - name: train
    num_bytes: 191210626.0
    num_examples: 78733
  download_size: 112697917
  dataset_size: 191210626.0
```