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Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning

Research
NLP methods
Docs live

This paper introduces multilingual adaptive fine-tuning (MAFT) applied to 17 of the most-resourced African languages, producing the AfroXLMR family of models. Removing non-African-script tokens cuts model size by roughly 50 percent while matching the accuracy of single-language adaptation on named entity recognition, topic classification and sentiment analysis.

Category
Research
Pricing
Free / open
Country
🌍 Pan-African
Last verified
10 Oct 2026
{
"name": "Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning",
"slug": "adapting-pre-trained-language-models-to-african-languages-via-multilingual-adaptive-fine-tuning",
"category": "RESEARCH",
"country": "Pan-African",
"docs_status": "LIVE",
"licensing_required": "NONE",
"verified": false,
"last_verified": "2026-10-10",
"website": "https://arxiv.org/abs/2204.06487",
"documentation_url": null
}
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Verification history

  • 10 Oct 2026 · live
  • 7 Oct 2026 · live
  • 4 Oct 2026 · live
  • 1 Oct 2026 · live
  • 28 Sept 2026 · live
  • 25 Sept 2026 · live
  • 22 Sept 2026 · live
  • 22 Sept 2026 · live

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Tags

nlp
african-languages
fine-tuning
language-models