Update dataset card with improved documentation

README.md

@@ -1,4 +1,5 @@
 ---
+pretty_name: ARIA Search Benchmark v2
 dataset_info:
   features:
   - name: dataset
@@ -34,4 +35,74 @@ configs:
   data_files:
   - split: benchmark
     path: data/benchmark-*
+language:
+  - en
+size_categories:
+  - 1K<n<10K
+tags:
+  - aria
+  - benchmark
+  - ml-research
+  - search
+  - retrieval
+task_categories:
+  - question-answering
 ---
+
+# ARIA Search Benchmark v2
+
+The ARIA Search Benchmark is part of the [ARIA benchmark suite](https://github.com/AlgorithmicResearchGroup/ARIA), a collection of closed-book benchmarks probing the ML knowledge that frontier models have internalized during training. This dataset tests whether models can answer factual questions about ML research papers, models, datasets, and benchmark results without access to external retrieval.
+
+## Dataset Summary
+
+- **Size**: 3,517 question-answer pairs
+- **Split**: `benchmark`
+- **Paper date range**: May 2023 to December 2024
+- **Coverage**: Spans models, datasets, and metrics across CV, NLP, audio, video, and multimodal domains
+
+## Dataset Structure
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `dataset` | string | Benchmark dataset referenced |
+| `model_name` | string | Model being evaluated |
+| `paper_title` | string | Source paper title |
+| `paper_date` | timestamp | Publication date |
+| `paper_url` | string | ArXiv paper URL |
+| `code_links` | list[string] | GitHub repository links |
+| `prompts` | string | Question/prompt text |
+| `answer` | string | Ground-truth answer |
+| `paper_text` | string | Full paper text |
+| `year_bin` | string | Year category for stratified evaluation |
+| `benchmark_split` | string | Benchmark split identifier |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AlgorithmicResearchGroup/aria-search-benchmark_v2-public", split="benchmark")
+
+for example in ds.select(range(5)):
+    print(f"Q: {example['prompts']}")
+    print(f"A: {example['answer']}")
+    print(f"Paper: {example['paper_title']}")
+    print()
+```
+
+## Related Resources
+
+- [ARIA Benchmark Suite](https://github.com/AlgorithmicResearchGroup/ARIA)
+- [Algorithmic Research Group - Open Source](https://algorithmicresearchgroup.com/opensource.html)
+
+## Citation
+
+```bibtex
+@misc{aria_search_benchmark_v2,
+    title={ARIA Search Benchmark v2},
+    author={Algorithmic Research Group},
+    year={2024},
+    publisher={Hugging Face},
+    url={https://huggingface.co/datasets/AlgorithmicResearchGroup/aria-search-benchmark_v2-public}
+}
+```