Datasets:

Kencorpus
/

KenSwQuAD

language:
  - sw
license: cc-by-4.0
task_categories:
  - question-answering
tags:
  - swahili
  - kiswahili
  - low-resource-languages
  - african-languages
  - extractive-qa
  - reading-comprehension
pretty_name: KenSwQuAD
size_categories:
  - 1K<n<10K

KenSwQuAD: A Question Answering Dataset for Swahili

Dataset Description

KenSwQuAD (Kenyan Swahili Question Answering Dataset) is a reading comprehension and question answering dataset for Swahili, a low-resource African language. The dataset contains 7,506 question-answer pairs derived from 1,441 unique Swahili contexts covering diverse topics including agriculture, education, technology, governance, and daily life in Kenya.

This dataset is designed for training and evaluating extractive question answering models on Swahili text.

Dataset Statistics

Metric	Count
Total QA Pairs	7,506
Unique Contexts	1,441
Avg QA Pairs per Context	5.21
Avg Question Length	41 characters
Avg Answer Length	14 characters
Avg Context Length	2,702 characters

Dataset Format

The dataset is distributed as Parquet files for optimal performance and compatibility:

Format: Apache Parquet (columnar storage)
Encoding: UTF-8
Compatibility: Works with datasets 4.0.0+ without custom loading scripts

Data Fields

Each record in the dataset contains:

id: string - Unique identifier for the QA pair (format: {story_id}_{qa_index})
story_id: string - Identifier for the source context/story (e.g., 3830_swa)
context: string - The passage/story from which questions are derived
question: string - The question in Swahili
answer: string - The answer text
paragraph_id: string - Optional paragraph/position indicator

Example Record

{
  'id': '3830_swa_0',
  'story_id': '3830_swa',
  'context': 'MANUFAA YA KILIMO KATIKA UIMARISHAJI WA UCHUMI WA KENYA Kilimo katika nchi yetu ya Kenya ni muhimu...',
  'question': 'Ni katika nchi ipi kilimo ni muhimu',
  'answer': 'Kenya',
  'paragraph_id': 'x'
}

Usage

Loading with 🤗 Datasets

Compatible with datasets 4.0.0+ (No trust_remote_code needed!)

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Kencorpus/KenSwQuAD")

# Access the training split
train = dataset['train']

# View first example
print(train[0])

Example: Training a QA Model

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer

# Load dataset
dataset = load_dataset("Kencorpus/KenSwQuAD")

# Load a multilingual model (supports Swahili)
model_name = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)

# Tokenize function
def tokenize_function(examples):
    return tokenizer(
        examples['question'],
        examples['context'],
        truncation=True,
        padding='max_length',
        max_length=384
    )

# Tokenize dataset
tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Train model (example)
training_args = TrainingArguments(
    output_dir="./kenswquad-model",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    num_train_epochs=3,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset['train'],
)

trainer.train()

Example: Exploring the Data

from datasets import load_dataset
import pandas as pd

# Load dataset
dataset = load_dataset("Kencorpus/KenSwQuAD")
df = pd.DataFrame(dataset['train'])

# Count QA pairs per story
qa_per_story = df.groupby('story_id').size().describe()
print("QA pairs per story distribution:")
print(qa_per_story)

# View sample context
sample = df[df['story_id'] == '3830_swa'].iloc[0]
print(f"\nContext: {sample['context'][:200]}...")
print(f"\nQuestion: {sample['question']}")
print(f"Answer: {sample['answer']}")

Dataset Topics

The contexts cover a wide variety of topics relevant to Kenyan society:

🌾 Agriculture & Farming - Crop cultivation, livestock, economic impact
🏫 Education - Schools, technology in education, student life
💻 Technology - Digital tools, internet, communication
🏛️ Governance & Politics - Leadership, government policies, elections
💰 Economy & Business - Trade, employment, economic development
🏥 Health - COVID-19, medical services, public health
🌍 Society & Culture - Daily life, traditions, social issues

Data Collection

The dataset was created by:

Collecting Swahili texts from various sources (articles, social media, essays)
Manual annotation of question-answer pairs by native Swahili speakers
Quality control and validation

Source Contexts:

2,585 texts from general sources (collected_data_text_swa_final_2585_out_of_2585)
324 texts from Twitter/social media (collected_data_text_swa_tweets_324_out_of_324)

Intended Uses

Primary Uses

Training extractive question answering models for Swahili
Evaluating reading comprehension capabilities
Transfer learning for low-resource African languages
Multilingual model evaluation

Out-of-Scope Uses

Generative question answering (dataset is designed for extractive QA)
Tasks requiring answers not present in the context
Languages other than Swahili

Limitations

Extractive nature: Answers are expected to be spans within the context
Domain coverage: While diverse, may not cover all Swahili domains
Answer length: Most answers are short (avg. 14 characters)
Regional variation: Primarily Kenyan Swahili, may not represent all Swahili dialects

Dataset Curators

Barack Wanjawa (University of Nairobi)
Lilian D.A. Wanzare (Maseno University)
Florence Indede (Maseno University)
Owen McOnyango (Maseno University)
Lawrence Muchemi (University of Nairobi)
Edward Ombui (Africa Nazarene University)

Citation

If you use this dataset in your research, please cite:

@article{wanjawa2022kencorpus,
  title={Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks},
  author={Wanjawa, Barack W. and Wanzare, Lilian D. and Indede, Florence and McOnyango, Owen and Ombui, Edward and Muchemi, Lawrence},
  journal={arXiv preprint arXiv:2208.12081},
  year={2022}
}

License

This dataset is licensed under CC-BY-4.0.

Acknowledgments

This dataset is part of the Kencorpus project, which aims to create NLP resources for low-resource Kenyan languages. We thank all annotators and contributors who made this dataset possible.