---
license: apache-2.0
language:
- ar
tags:
- islamic-finance
- fatwa
- question-answering
- evaluation
- benchmark
- arabic
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- text-generation
pretty_name: "Fatwa QA Evaluation Dataset"
---

# Fatwa QA Evaluation Dataset

## Dataset Description

This dataset contains Islamic finance and jurisprudence fatwa question-answer pairs for **evaluating** Arabic language models. This is an open-ended QA evaluation benchmark where models generate free-form answers.

## Dataset Statistics

- **Total Samples**: 2,000
- **Average Question Length**: 243.9 characters
- **Average Answer Length**: 492.3 characters

## Dataset Structure

### Data Fields

- `id`: Unique identifier (format: `fatwa_eval_XXXXX`)
- `prompt`: Full evaluation prompt (instruction + question + الإجابة:)
- `question`: Original question text
- `answer`: Ground truth answer
- `category`: Islamic finance category
- `question_length`: Character count of the question
- `answer_length`: Character count of the answer

### Categories

- **zakat**: 792 samples
- **riba**: 407 samples
- **murabaha**: 234 samples
- **gharar**: 149 samples
- **waqf**: 124 samples
- **ijara**: 102 samples
- **maysir**: 64 samples
- **musharaka**: 44 samples
- **mudharaba**: 40 samples
- **takaful**: 38 samples
- **sukuk**: 6 samples

### Prompt Format
```
بناءً على أحكام الشريعة الإسلامية والفقه الإسلامي، أجب على السؤال التالي بطريقة مفصلة ومدعمة بالأدلة عند الإمكان.  السؤال: [QUESTION]  الإجابة:
```

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("SahmBenchmark/fatwa-qa-evaluation")

# Access evaluation data
for example in dataset['test']:
    print(f"ID: {example['id']}")
    print(f"Prompt: {example['prompt']}")
    print(f"Question: {example['question']}")
    print(f"Answer: {example['answer']}")
    print(f"Category: {example['category']}")
```

### Evaluation Example
```python
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load dataset and model
dataset = load_dataset("SahmBenchmark/fatwa-qa-evaluation")
model_name = "your-model-name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate predictions
def generate_answer(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=512)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Evaluate
for example in dataset['test']:
    prediction = generate_answer(example['prompt'])
    ground_truth = example['answer']
    # Compare prediction with ground_truth using your metrics
```

## Categories

- **zakat**: Islamic almsgiving
- **riba**: Interest/usury-related rulings
- **murabaha**: Cost-plus financing
- **gharar**: Uncertainty in contracts
- **waqf**: Islamic endowment
- **ijara**: Islamic leasing
- **maysir**: Gambling-related rulings
- **musharaka**: Partnership financing
- **mudharaba**: Profit-sharing partnership
- **takaful**: Islamic insurance
- **sukuk**: Islamic bonds

## Related Datasets

- [Fatwa Training Dataset](https://huggingface.co/datasets/SahmBenchmark/fatwa-training_standardized_new): Training data for this evaluation benchmark
- [Fatwa MCQ Evaluation](https://huggingface.co/datasets/SahmBenchmark/fatwa-mcq-evaluation_standardized): Multiple choice evaluation version

## Citation
```bibtex
@dataset{fatwa_qa_evaluation,
  title={Fatwa QA Evaluation Dataset},
  author={SahmBenchmark},
  year={2025},
  url={https://huggingface.co/datasets/SahmBenchmark/fatwa-qa-evaluation}
}
```

## License

Apache 2.0 License