README.md

---
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

```python
{
  '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!)

```python
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

```python
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

```python
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:
1. Collecting Swahili texts from various sources (articles, social media, essays)
2. Manual annotation of question-answer pairs by native Swahili speakers
3. 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:

```bibtex
@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}
}
```

---

## Links

- **Research Paper**: https://arxiv.org/abs/2208.12081
- **Dataverse**: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OTL0LM
- **ResearchGate**: https://www.researchgate.net/publication/371767223
- **Semantic Scholar**: https://www.semanticscholar.org/paper/8cf70c5cd8b195ed7a399ea2cdc0b0e8f08c61ce

---

## 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.