AfrimBert-QA

Model Description

AfrimBert-QA is a fine-tuned version of amidblue/mBertKE trained on the amidblue/AfriQuAD dataset. It is designed for extractive question answering — both monolingual and cross-lingual — across African languages.

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Supported Languages

The model supports 10+ African languages, with primary support for:

Language	Region
Luo	Kenya/Uganda
Kalenjin	Kenya
Kikuyu	Kenya
Gusii	Kenya
Swahili	East Africa

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Training Data

The model was trained on a combination of the following datasets:

• KENSQUAD — Kenyan extractive QA dataset
• AFRIQA — Pan-African QA benchmark
• Custom data — Additional data collected for languages not covered by AFRIQA and KENSQUAD

Cross-lingual QA Dataset Stats

Type	Approximate Size
Generated cross-lingual QA pairs	~800 examples
Translated cross-lingual QA pairs	~800 examples

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Usage

from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="amidblue/AfrimBert-QA")

# Example context and question in Luo
context = "Ji mang'eny ok winjre gi kaka chama mar ODM iriembo. Tinde nitie koko mang'eny e chama no."
question = "Chama mane ema ji oko hero kaka iriembo?"

# Run the model
result = qa_pipeline(question=question, context=context)

print(f"Question:         {question}")
print(f"Answer:           {result['answer']}")
print(f"Confidence Score: {result['score']:.4f}")
print(f"Start: {result['start']}, End: {result['end']}")

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Citation

If you use this model or its associated dataset, please cite:

@misc{afrimbert-qa,
  author    = {Theophilus Linicon Owiti and Alukwe Jones Terah},
  title     = {AfrimBert-QA: Extractive Question Answering for African Languages},
  year      = {2026},
  publisher = {Hugging Face},
  note      = {Carnegie Mellon University, Amidblue},
  url       = {https://huggingface.co/amidblue/AfrimBert-QA}
}

Authors:

• Theophilus Linicon Owiti — Carnegie Mellon University / Amidblue
• Alukwe Jones Terah — Amidblue

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Model Card Authors

Theophilus Linicon Owiti & Alukwe Jones Terah