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README.md

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+---
+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.