We're looking for testers: try AfriAnnotate early

We're opening up AfriAnnotate, our open annotation platform, for early testing. If you annotate text or speech for African languages, work on community datasets, or teach data-collection workflows, we'd love your help shaping what we ship in v1.

What AfriAnnotate is

AfriAnnotate is an Apache 2.0, mobile-first, offline-capable Progressive Web App for African-language NLP data collection. We built it because most existing annotation tools assume desktop browsers, fast connectivity, and Latin script. Those assumptions don't hold for the African contexts we work in.

In its current shape, the tool supports:

• Sentiment annotation (e.g., AfriSenti-style three-way labelling)
• Named entity recognition with custom entity sets
• LLM-evaluation workflows to score model output for accuracy, fluency, and safety
• Inter-annotator agreement dashboards (Fleiss' κ, Krippendorff's α)
• Virtual keyboards for non-Latin scripts (Ge'ez and others)
• Offline-first capture with background sync when connectivity returns

Pilot deployments are running at Bayero University, Kano and Bahir Dar University ICT4D.

Why we need testers now

We've validated the architecture and the core annotation flows internally. What we need next is real users on real tasks: annotators using the tool on their own phones and laptops, on their own networks, in their own languages, to surface the gaps we can't see from inside the team.

Specifically:

• Usability: what feels confusing? what slows you down?
• Scripts and keyboards: does typing in your language work the way you expect?
• Offline behaviour: does the sync feel reliable on patchy 3G?
• Mobile vs desktop: which workflows feel right on each?
• Accessibility: anything that breaks for screen readers, low vision, or limited motor control?

Who we're looking for

You don't need to be a senior researcher. We're especially interested in:

• Annotators doing labelling work for African-language datasets (paid or volunteer)
• Students learning NLP or linguistics
• Researchers running annotation projects (your team becomes a pilot)
• Educators who teach data-collection workflows
• Native speakers of any African language (we want broad script and morphology coverage)

If your work touches African-language data in any way, you fit the profile.

What testing looks like

• Time commitment: 1–3 hours over a 2-week window. You can spread it however you want.
• Tasks: complete a small annotation project we provide (or use the tool on a project of your own).
• Feedback: a 15-minute call OR a written form, your choice.
• No NDA, no signup paperwork.
• All testers are credited in the v1 release notes if they want to be (or stay anonymous).

How to sign up

Three ways, pick whichever fits:

1. Discord: say hi in #afriannotate and a maintainer will reach out
2. GitHub Discussions: open a thread in the project's discussions with a brief intro
3. Email: write to the team via the address on the About page (contact the Masakhane community)

We'll get back within 48 hours with onboarding details and a tester invite.

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If you can't test but know someone who would be great, please forward this. The strongest signal of community-built infrastructure is community-led testing.