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README.md

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+# Summarization Fine-tuning Dataset
+
+A dataset of 2000 examples for fine-tuning small language models on summarization tasks.
+
+## Statistics
+
+- **Total examples**: 2000
+- **Train examples**: 1600 (80.0%)
+- **Validation examples**: 200 (10.0%)
+- **Test examples**: 200 (10.0%)
+
+## Dataset Distribution
+
+| Dataset | Count | Percentage |
+|---------|-------|------------|
+| xsum | 2000 | 100.0% |
+
+## Format
+
+The dataset is provided in alpaca format.
+
+## Configuration
+
+- **Maximum tokens**: 2000
+- **Tokenizer**: gpt2
+- **Random seed**: 42
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("YOUR_USERNAME/summarization-finetune-10k")
+
+# Access the splits
+train_data = dataset["train"]
+val_data = dataset["validation"]
+test_data = dataset["test"]
+```

dataset_card.json

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+{
+  "language": [
+    "en"
+  ],
+  "license": "cc-by-4.0",
+  "pretty_name": "Summarization Fine-tuning Dataset"
+}

dataset_info.json

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+{
+  "name": "Summarization Fine-tuning Dataset",
+  "description": "A dataset for fine-tuning small language models on summarization tasks",
+  "format": "alpaca",
+  "statistics": {
+    "total_examples": 2000,
+    "train_examples": 1600,
+    "val_examples": 200,
+    "test_examples": 200,
+    "dataset_distribution": {
+      "xsum": {
+        "count": 2000,
+        "percentage": 100.0
+      }
+    }
+  },
+  "configuration": {
+    "max_tokens": 2000,
+    "tokenizer": "gpt2",
+    "seed": 42
+  }
+}