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--- |
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license: apache-2.0 |
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language: |
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- ar |
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tags: |
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- islamic-finance |
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- fatwa |
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- question-answering |
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- evaluation |
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- benchmark |
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- arabic |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- question-answering |
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- text-generation |
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pretty_name: "Fatwa QA Evaluation Dataset" |
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--- |
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# Fatwa QA Evaluation Dataset |
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## Dataset Description |
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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. |
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## Dataset Statistics |
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- **Total Samples**: 2,000 |
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- **Average Question Length**: 243.9 characters |
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- **Average Answer Length**: 492.3 characters |
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## Dataset Structure |
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### Data Fields |
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- `id`: Unique identifier (format: `fatwa_eval_XXXXX`) |
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- `prompt`: Full evaluation prompt (instruction + question + الإجابة:) |
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- `question`: Original question text |
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- `answer`: Ground truth answer |
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- `category`: Islamic finance category |
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- `question_length`: Character count of the question |
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- `answer_length`: Character count of the answer |
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### Categories |
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- **zakat**: 792 samples |
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- **riba**: 407 samples |
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- **murabaha**: 234 samples |
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- **gharar**: 149 samples |
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- **waqf**: 124 samples |
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- **ijara**: 102 samples |
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- **maysir**: 64 samples |
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- **musharaka**: 44 samples |
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- **mudharaba**: 40 samples |
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- **takaful**: 38 samples |
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- **sukuk**: 6 samples |
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### Prompt Format |
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``` |
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بناءً على أحكام الشريعة الإسلامية والفقه الإسلامي، أجب على السؤال التالي بطريقة مفصلة ومدعمة بالأدلة عند الإمكان. السؤال: [QUESTION] الإجابة: |
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``` |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("SahmBenchmark/fatwa-qa-evaluation") |
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# Access evaluation data |
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for example in dataset['test']: |
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print(f"ID: {example['id']}") |
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print(f"Prompt: {example['prompt']}") |
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print(f"Question: {example['question']}") |
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print(f"Answer: {example['answer']}") |
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print(f"Category: {example['category']}") |
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``` |
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### Evaluation Example |
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```python |
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from datasets import load_dataset |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load dataset and model |
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dataset = load_dataset("SahmBenchmark/fatwa-qa-evaluation") |
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model_name = "your-model-name" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Generate predictions |
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def generate_answer(prompt): |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=512) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Evaluate |
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for example in dataset['test']: |
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prediction = generate_answer(example['prompt']) |
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ground_truth = example['answer'] |
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# Compare prediction with ground_truth using your metrics |
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``` |
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## Categories |
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- **zakat**: Islamic almsgiving |
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- **riba**: Interest/usury-related rulings |
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- **murabaha**: Cost-plus financing |
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- **gharar**: Uncertainty in contracts |
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- **waqf**: Islamic endowment |
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- **ijara**: Islamic leasing |
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- **maysir**: Gambling-related rulings |
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- **musharaka**: Partnership financing |
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- **mudharaba**: Profit-sharing partnership |
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- **takaful**: Islamic insurance |
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- **sukuk**: Islamic bonds |
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## Related Datasets |
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- [Fatwa Training Dataset](https://huggingface.co/datasets/SahmBenchmark/fatwa-training_standardized_new): Training data for this evaluation benchmark |
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- [Fatwa MCQ Evaluation](https://huggingface.co/datasets/SahmBenchmark/fatwa-mcq-evaluation_standardized): Multiple choice evaluation version |
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## Citation |
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```bibtex |
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@dataset{fatwa_qa_evaluation, |
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title={Fatwa QA Evaluation Dataset}, |
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author={SahmBenchmark}, |
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year={2025}, |
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url={https://huggingface.co/datasets/SahmBenchmark/fatwa-qa-evaluation} |
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} |
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``` |
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## License |
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Apache 2.0 License |
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