Datasets:

ImhotepSystems
/

AfriHealth-QA

dataset_info:
  features:
    - name: id
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: language_country
      dtype: string
  splits:
    - name: train
    - name: validation
    - name: test_holdout
  download_size: 25400000
  dataset_size: 29500000
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test_holdout
        path: data/test_holdout-*
license: cc-by-4.0
task_categories:
  - question-answering
  - conversational
language:
  - ak
  - am
  - lg
  - sw
  - en
tags:
  - health-nlp
  - medical
  - adolescent-health
  - sub-saharan-africa
  - multilingual
  - african-languages
pretty_name: AfriHealth-QA

AfriHealth-QA

A Multilingual Health Question-Answering Dataset for African Languages

Dataset Description

This dataset provides a curated collection of health question-and-answer pairs focused on Adolescent Sexual and Reproductive Health (ASRH), maternal care, and infectious disease dynamics across Sub-Saharan Africa. Curated via crowdsourcing vectors by the HASH Consortium, it bridges linguistic variations across local vernacular, code-mixed environments, and standard formats.

Domain Coverage

Languages: Akan (Aka_Gha), Amharic (Amh_Eth), Luganda (Lug_Uga), Swahili (Swa_Ken), and contextual English variants (Eng_Uga, Eng_Gha, Eng_Eth, Eng_Ken).
Countries: Ghana, Ethiopia, Uganda, Kenya
Core Features: Real-world colloquial phrasings, dense morphological code-switching, and clinical consensus answers.

Dataset Summary

Split	Examples	Description
Train	~29,815	Labeled question-answer pairs
Validation	~6,686	Labeled question-answer pairs
Test (Holdout)	~2,618	Unlabeled questions for private benchmarking
Total	~39,119

Data Schema

Column	Type	Description
`id`	string	Unique identifier (format: `ID_XX_Language_Country_Hash`)
`question`	string	Health-related question in the specified language
`answer`	string	Expert answer (available in train/val, not in test)
`language_country`	string	Language-country code (e.g., `Aka_Gha` = Akan, Ghana)

Language Reference

Code	Language	Country	ISO 639
`Aka_Gha`	Akan (Twi)	Ghana	`ak`
`Amh_Eth`	Amharic	Ethiopia	`am`
`Eng_Eth`	English	Ethiopia	`en`
`Eng_Gha`	English	Ghana	`en`
`Eng_Ken`	English	Kenya	`en`
`Eng_Uga`	English	Uganda	`en`
`Lug_Uga`	Luganda	Uganda	`lg`
`Swa_Ken`	Swahili	Kenya	`sw`

Data Source

This dataset originates from the Zindi Africa Multilingual Health QA Challenge, curated for training multilingual health chatbots serving African communities.

Intended Use

Training multilingual health question-answering models
Fine-tuning LLMs for African health communication
Benchmarking NLP models on multilingual health QA
Research in low-resource language NLP and health informatics

Key Features

8 language-country subsets covering Akan, Amharic, Luganda, Swahili, and English
Code-mixed text: Natural switching between local languages and English
Expert answers: Long-form, detailed responses (avg ~76 words)
Topic-focused: All questions relate to adolescent sexual and reproductive health

Example

Language: Akan (Ghana)
Question: Ɔkwan bɛn so na mmabunbɛtumi aboa wɔn mfɛfoɔ a nsa anaa nnubɔne ama wɔayɛ wɔn ayayadeɛ?
Answer: Mmabun betumi aboa atipɛnfo a ebia nsa anaa nnubɔne ama wɔayɛ wɔn ayayadeɛ so denam: Nkate fam mmoa a wɔde bɛma...

Usage

from datasets import load_dataset

dataset = load_dataset("ImhotepSystems/AfriHealth-QA")

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test_holdout"]

# Filter by language
akan_data = dataset["train"].filter(lambda x: x["language_country"] == "Aka_Gha")

# Example usage
for example in dataset["train"].select(range(3)):
    print(f"Q: {example['question']}")
    print(f"A: {example['answer'][:100]}...")
    print()

Training Models

This dataset is designed to train imhotep-healthqa models — open-source health QA systems for African languages:

# Fine-tuning example (pseudocode)
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ImhotepSystems/imhotep-healthqa")
tokenizer = AutoTokenizer.from_pretrained("ImhotepSystems/imhotep-healthqa")

# Fine-tune on AfriHealth-QA
# ...

Citation

@misc{afrihealthqa2026,
  title={AfriHealth-QA: A Multilingual Health Question-Answering Dataset for African Languages},
  author={Imhotep Systems},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/ImhotepSystems/AfriHealth-QA}
}

License

Please refer to the original dataset terms from Zindi Africa. If you use this dataset, please cite both the original source and Imhotep Systems.

Contact

Imhotep Systems — Building open-source AI for African health.

HuggingFace: ImhotepSystems