Add dataset card

README.md

@@ -1,40 +1,69 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: prompt
-    dtype: string
-  - name: full_text
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: report_type
-    dtype: string
-  - name: file_name
-    dtype: string
-  - name: split
-    dtype: string
-  - name: text_length
-    dtype: int64
-  - name: summary_length
-    dtype: int64
-  - name: compression_ratio
-    dtype: float64
-  splits:
-  - name: validation
-    num_bytes: 189629
-    num_examples: 20
-  - name: test
-    num_bytes: 179696
-    num_examples: 20
-  download_size: 207759
-  dataset_size: 369325
-configs:
-- config_name: default
-  data_files:
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- ar
+license: apache-2.0
+task_categories:
+- summarization
+- text-generation
+pretty_name: Financial Reports Extractive Summarization Evaluation Dataset
+tags:
+- finance
+- summarization
+- extractive
+- evaluation
+- benchmark
+- arabic
 ---
+
+# Financial Reports Extractive Summarization Evaluation Dataset
+
+Validation and test splits for evaluating models on Arabic financial reports extractive summarization.
+
+## Dataset Structure
+
+- **Format**: Simple prompt-answer pairs
+- **Validation**: ~20 examples (10%)
+- **Test**: ~20 examples (10%)
+- **Language**: Arabic
+- **Domain**: Financial reports and market news
+
+## Fields
+
+- `id`: Unique identifier
+- `prompt`: The summarization prompt
+- `full_text`: Complete financial report
+- `answer`: Ground truth extractive summary
+- `report_type`: Type of report
+- `file_name`: Original file
+- `split`: 'validation' or 'test'
+- `text_length`: Full text length
+- `summary_length`: Summary length
+- `compression_ratio`: Compression percentage
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("SahmBenchmark/financial-reports-extractive-summarization_eval")
+
+# Access splits
+val_data = dataset['validation']
+test_data = dataset['test']
+
+# For evaluation
+for example in test_data:
+    model_output = model.generate(example['prompt'])
+    ground_truth = example['answer']
+    
+    # Calculate ROUGE scores
+    rouge_score = calculate_rouge(model_output, ground_truth)
+```
+
+## Evaluation Metrics
+
+- ROUGE-1, ROUGE-2, ROUGE-L
+- Compression ratio accuracy
+- Extractive accuracy (sentences from original)
+
+For training data, see: `SahmBenchmark/financial-reports-extractive-summarization_train`