Introduction
This playbook is open source and community-owned. You don't need to write a whole chapter to help. Fixing an error, translating a page, or sharing what worked on a real project all count. See Contribute.
The bullet was the means of the physical subjugation. Language was the means of the spiritual subjugation.
Ngũgĩ wa Thiong'o, Decolonising the Mind: The Politics of Language in African Literature (1986)
Africa is home to almost a third of the world's living languages: about 2,140 of the roughly 7,160 spoken on Earth (Ethnologue, 2024). Most of them remain out of reach of the systems now reshaping how the rest of the world reads, writes, searches, translates, and speaks. Even the best-supported, such as Swahili, Hausa and Amharic, lag far behind high-resource languages. When a language model mangles Yorùbá, Chichewa, or Wolof, the cause is usually not the model but the data. The text and speech these systems learn from barely exist in a usable form.
Joshi et al. (2020) sort the world's languages into six tiers by how many resources they have. The bottom tier, the left-behinds, with essentially no labelled data and little prospect of being served by current methods, holds the overwhelming majority of languages, and African languages crowd into it. Most have no annotated corpus, no benchmark, no tools. Many have tens of millions of speakers. What they lack is data, because almost no one has built it. The picture still holds, though the field now treats low-resource as multidimensional: a matter of tools, speakers, funding, and institutional support as much as raw data, with no single agreed definition (Ranathunga & de Silva, 2022; Nigatu et al., 2024).
Scraping will not fix this
When a language has no data, the instinct is to go and scrape more of it: crawl a wider slice of the web and trust that coverage will follow. For African languages, that instinct fails.
The web does not contain much African-language text, and what it contains is thin and noisy. When Kreutzer et al. (2022) audited the large multilingual crawls everyone trains on, they found that for many low-resource languages a large share of the data was mislabelled, machine-translated, or not language at all. At the tail, quality collapses along with quantity.
The only sure way to get high-quality data for African languages is to build it with the people who speak them, the people who know the words, the grammar, the idioms, and the culture. One of the main obstacles in AfricaNLP is that the people who speak these languages are not the ones building the data. The people who build it often cannot tell what is correct, what is offensive, or what is missing. They do not know what matters to the communities behind the language, or how to keep the data they collect from causing harm.
That gap has real consequences. Data built without its speakers can look clean while being quietly wrong, and any model trained on it inherits every mistake. Such errors spread into search results, translations, and the everyday tools that millions of people are starting to depend on. Getting the data right decides whether a language is served well, served badly, or left out of these tools altogether.
This playbook is about how to fix that problem. It is a practical, opinionated, step-by-step guide to building high-quality datasets for African languages, drawing on the direct experience of the people who speak and understand them. The playbook is built by the people who know the languages, for the people who want to build datasets for them. It is about how to do it right, and how to do it safely.
The field is growing, the data is not keeping up
Over the past two decades, AfricaNLP has grown from a niche interest into an established field. Research output has grown more than tenfold, from about 20 papers a year in 2006 to nearly 300 in 2024 (Belay et al., 2025).
But more papers have not meant more data. More than half of this work proposes new methods, while only about one in five contributions introduces a new dataset (Belay et al., 2025). We are getting better at building models faster than we are building the data they learn from.
Methods and datasets are not made the same way. A method can often be reused across languages; a dataset has to be built for each one, from scratch, by people who speak it. That means recruiting annotators, writing guidelines, running quality control, and securing consent. It is slow, unglamorous work, and it is rarely funded, least of all for African languages.
This playbook exists to make that work easier. It walks through each stage of building a dataset: deciding what to collect, designing the annotation, checking quality, documenting, and releasing. It is written for the real conditions of African-language NLP: limited data, multilingual teams, scarce funding, and communities who should remain the owners of what they help create.
Why we wrote this playbook
Almost every guide to building datasets quietly assumes English, a generous budget, and a problem someone has already solved once. Little of that holds when you are starting a corpus for a language with no prior resources, a volunteer team, and decisions to make that the literature never covers.
The AfriPlaybook is the manual we wish we had had. Many of us learned to build datasets for African languages the hard way, through trial and error, with little written down and few people to ask. That is part of why datasets have lagged so far behind methods. This playbook collects what we learned so that the next team can start sooner, avoid the mistakes we made, and build data the world can trust and reuse.
The playbook will keep growing as the community shares what it learns. The more we pool our experience, the cheaper it becomes to build data, and the sooner datasets can catch up with methods.
Built in the open
This playbook is open source, maintained by the Waraka community, Masakhane, and AfricaNLP researchers. It is not just for researchers. It is for everyone who builds datasets for African languages: volunteers, students, community organizers, and professionals alike. The people who build datasets know best what a guide like this should say, so it is only as good as the people who contribute to it. There are many ways to help:
- Write a chapter or section that fills a gap.
- Review existing chapters: correct an error, sharpen a claim, add a reference.
- Share a case study from a real project, including what went wrong.
- Open a discussion when you disagree with an approach. Disagreement makes the guide better.
Start with the contribution guide, raise an idea in GitHub Discussions, or join us on Discord. If you build datasets for African languages, or want to learn how, you are already part of who this is for. Come and build it with us.
How to cite this playbook
If the AfriPlaybook informs your research, teaching, or project, please cite it.
BibTeX:
@misc{waraka2026playbook,
author = {{Waraka Community}},
title = {AfriPlaybook: A Practical Guide to Building High-Quality Datasets for African Languages},
year = {2026},
publisher = {Waraka Community},
url = {https://afriplaybook.waraka.org/},
note = {Open-source community resource}
}
Plain text (APA-style):
Waraka Community. (2026). AfriPlaybook: A Practical Guide to Building High-Quality Datasets for African Languages. https://afriplaybook.waraka.org/
For other formats (MLA, Chicago, etc.) and a machine-readable CITATION.cff, see the /cite page.
If you reference a specific chapter, please include the chapter title and its URL.
