Datasets:

SahmBenchmark
/

fatwa-training_standardized_new

license: apache-2.0
language:
  - ar
tags:
  - islamic-finance
  - fatwa
  - question-answering
  - training
  - instruction-tuning
  - arabic
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
  - text-generation
pretty_name: Fatwa Training Dataset (Standardized)
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: conversations
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
    - name: category
      dtype: string
    - name: is_referral
      dtype: string
    - name: question_length
      dtype: int64
    - name: answer_length
      dtype: int64
  splits:
    - name: train
      num_bytes: 15481402
      num_examples: 9953
  download_size: 6512899
  dataset_size: 15481402

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

Prompt Templates

Each fatwa appears 3 times with different prompt styles:

Formal Style: "بناءً على أحكام الشريعة الإسلامية والفقه الإسلامي، أجب على السؤال التالي..."
Concise Style: "أجب على السؤال التالي وفقاً لأحكام الشريعة الإسلامية..."
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)

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