Add dataset card

README.md

@@ -1,149 +1,133 @@
 ---
+license: apache-2.0
 language:
 - ar
-license: apache-2.0
-task_categories:
-- question-answering
-- text-generation
-pretty_name: Fatwa Q&A Evaluation Dataset
 tags:
-- islamic-jurisprudence
+- islamic-finance
 - fatwa
-- sharia
-- fiqh
+- question-answering
 - evaluation
 - benchmark
 - arabic
-- sahm-benchmark
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: prompt
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: category
-    dtype: string
-  - name: question_length
-    dtype: int64
-  - name: answer_length
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 8159968
-    num_examples: 4000
-  download_size: 3511512
-  dataset_size: 8159968
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
+size_categories:
+- 1K<n<10K
+task_categories:
+- question-answering
+- text-generation
+pretty_name: "Fatwa QA Evaluation Dataset"
 ---
 
-# Fatwa Q&A Evaluation Dataset
+# Fatwa QA Evaluation Dataset
 
 ## Dataset Description
 
-Evaluation dataset for Islamic jurisprudence Q&A, extracted from the Fatwa MCQ evaluation dataset. This dataset contains validation and test splits for evaluating language models on Islamic legal rulings and religious guidance.
+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 Summary
+## Dataset Statistics
 
-- **Language:** Arabic
-- **Size:** 250 evaluation examples (125 validation, 125 test)
-- **Domain:** Islamic jurisprudence, Sharia law, Fiqh
-- **Format:** Simple prompt-answer pairs
-- **Task:** Fatwa generation evaluation
+- **Total Samples**: 4,000
+- **Average Question Length**: 237.3 characters
+- **Average Answer Length**: 488.9 characters
 
 ## Dataset Structure
 
 ### Data Fields
 
-- `id`: Unique identifier for each example
-- `prompt`: The full prompt with Islamic context and question
-- `question`: The original question text
-- `answer`: Ground truth fatwa/religious ruling
-- `category`: Category of the fatwa (murabaha, ijara, takaful, sukuk, etc.)
-- `question_length`: Length of the question in characters
-- `answer_length`: Length of the answer in characters
-
-### Data Splits
-
-- **Validation**: 125 examples (50%)
-- **Test**: 125 examples (50%)
-
-## Categories
-
-The dataset covers various Islamic finance and jurisprudence topics:
-- Murabaha (Islamic financing)
-- Ijara (Islamic leasing)
-- Takaful (Islamic insurance)
-- Sukuk (Islamic bonds)
-- General Islamic jurisprudence
-
-## Example
-
-```json
-{
-  "id": "fatwa_eval_00009",
-  "prompt": "بناءً على أحكام الشريعة الإسلامية والفقه الإسلامي، أجب على السؤال التالي بفتوى شرعية مفصلة ومدعمة بالأدلة عند الإمكان.\n\nالسؤال: [question text]\n\nالفتوى الشرعية:",
-  "question": "[Original question]",
-  "answer": "[Ground truth fatwa]",
-  "category": "murabaha",
-  "question_length": 234,
-  "answer_length": 567
-}
+- `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**: 1616 samples
+- **riba**: 818 samples
+- **murabaha**: 466 samples
+- **gharar**: 292 samples
+- **waqf**: 246 samples
+- **ijara**: 196 samples
+- **maysir**: 125 samples
+- **musharaka**: 84 samples
+- **mudharaba**: 78 samples
+- **takaful**: 68 samples
+- **sukuk**: 11 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
 
-# Load the evaluation dataset
 dataset = load_dataset("SahmBenchmark/fatwa-qa-evaluation")
 
-# Access splits
-val_data = dataset['validation']
-test_data = dataset['test']
+# 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
 
-# Evaluation example
-for example in test_data:
-    model_output = model.generate(example['prompt'])
+# 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']
-    
-    # Evaluate the generated fatwa
-    score = evaluate_fatwa(model_output, ground_truth)
+    # Compare prediction with ground_truth using your metrics
 ```
 
-## Evaluation Considerations
+## Categories
 
-When evaluating models on this dataset:
-- Consider theological accuracy and adherence to Islamic principles
-- Evaluate the quality of evidence and references provided
-- Assess clarity and comprehensiveness of the fatwa
-- Be aware that fatwas may vary based on different schools of Islamic thought
-- Consider using semantic similarity metrics in addition to exact matching
+- **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
 
-- Training dataset: `SahmBenchmark/fatwa-training_standardized`
-- MCQ evaluation: `SahmBenchmark/fatwa-mcq-evaluation`
+- [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_2025,
-  title={Fatwa Q&A Evaluation Dataset},
+@dataset{fatwa_qa_evaluation,
+  title={Fatwa QA Evaluation Dataset},
   author={SahmBenchmark},
   year={2025},
-  publisher={Hugging Face}
+  url={https://huggingface.co/datasets/SahmBenchmark/fatwa-qa-evaluation}
 }
 ```
 
-## Disclaimer
+## License
 
-This dataset is for academic and research purposes. Religious rulings should be sought from qualified Islamic scholars for practical application.
+Apache 2.0 License