SahmBenchmark/fatwa-training_standardized_new

Fatwa Training Dataset (Standardized)

Dataset Description

This dataset contains Islamic finance and jurisprudence fatwa question-answer pairs in a standardized conversation format for training Arabic language models. Each original sample has been augmented with 3 different prompt templates to increase training diversity.

Dataset Statistics

• —Total Samples: 9,953
• —Unique Fatwas: 6,212
• —Prompt Variations: 3 per fatwa
• —Average Question Length: 230.0 characters
• —Average Answer Length: 493.6 characters

Dataset Structure

Data Fields

• —id: Unique identifier for each fatwa (format: fatwa_XXXXX)
• —conversations: List of conversation turns in chat format
• —content: The text content
• —role: Either "human" (question) or "agent" (answer)
• —category: Islamic finance category
• —is_referral: Whether the fatwa is mainly a referral (YES/NO)
• —question_length: Character count of the original question
• —answer_length: Character count of the answer

Categories

• —zakat: 4096 samples
• —riba: 2047 samples
• —murabaha: 1155 samples
• —gharar: 711 samples
• —waqf: 606 samples
• —ijara: 469 samples
• —maysir: 308 samples
• —musharaka: 198 samples
• —mudharaba: 188 samples
• —takaful: 149 samples
• —sukuk: 26 samples

Prompt Templates

Each fatwa appears 3 times with different prompt styles:

1. 1.Formal Style: "بناءً على أحكام الشريعة الإسلامية والفقه الإسلامي، أجب على السؤال التالي..."
2. 2.Concise Style: "أجب على السؤال التالي وفقاً لأحكام الشريعة الإسلامية..."
3. 3.Expert Persona: "أنت عالم متخصص في الفقه الإسلامي والمعاملات المالية..."

Usage

from datasets import load_dataset

dataset = load_dataset("SahmBenchmark/fatwa-training_standardized_new")

# Access training data
for example in dataset['train']:
    print(f"ID: {example['id']}")
    print(f"Human: {example['conversations'][0]['content']}")
    print(f"Agent: {example['conversations'][1]['content']}")
    print(f"Category: {example['category']}")

For Fine-tuning

from datasets import load_dataset

dataset = load_dataset("SahmBenchmark/fatwa-training_standardized_new")

def format_for_training(example):
    human_msg = example['conversations'][0]['content']
    agent_msg = example['conversations'][1]['content']
    return {"text": f"### Human: {human_msg}\n\n### Assistant: {agent_msg}"}

formatted_dataset = dataset.map(format_for_training)

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

Citation

@dataset{fatwa_training_standardized,
  title={Fatwa Training Dataset (Standardized)},
  author={SahmBenchmark},
  year={2025},
  url={https://huggingface.co/datasets/SahmBenchmark/fatwa-training_standardized_new}
}

License

Apache 2.0 License