Add ShapeCodeBench dataset card

README.md

@@ -1,57 +1,68 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_file_name
-    dtype: string
-  - name: image_sha256
-    dtype: string
-  - name: sample_id
-    dtype: string
-  - name: split
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: seed
-    dtype: int64
-  - name: image_size
-    dtype: int64
-  - name: num_shapes
-    dtype: int64
-  - name: shape_inventory
-    struct:
-    - name: circle
-      dtype: int64
-    - name: filled_circle
-      dtype: int64
-    - name: filled_square
-      dtype: int64
-    - name: square
-      dtype: int64
-  - name: ground_truth_program
-    dtype: string
-  - name: render_config
-    struct:
-    - name: background_color
-      dtype: int64
-    - name: canvas_size
-      dtype: int64
-    - name: foreground_color
-      dtype: int64
-    - name: image_mode
-      dtype: string
-    - name: renderer_semantics
-      dtype: string
-  splits:
-  - name: eval
-    num_bytes: 425157
-    num_examples: 150
-  download_size: 408616
-  dataset_size: 425157
-configs:
-- config_name: default
-  data_files:
-  - split: eval
-    path: data/eval-*
+pretty_name: ShapeCodeBench eval_v1
+license: cc-by-4.0
+size_categories:
+- n<1K
+tags:
+- image
+- synthetic
+- benchmark
+- code-generation
+- program-synthesis
+- arxiv:2605.11680
 ---
+
+# ShapeCodeBench eval_v1
+
+This dataset is the frozen `eval_v1` reporting split for
+[ShapeCodeBench](https://github.com/shivamk3r/shape-code-bench), a synthetic
+benchmark for testing whether multimodal models can reconstruct executable
+drawing programs from rendered shape images.
+
+It contains 150 grayscale `512x512` PNG images: 50 `easy`, 50 `medium`, and
+50 `hard` examples. Each row includes the rendered image, the canonical
+ShapeCodeBench DSL program that generated it, generation metadata, render
+configuration, and the SHA256 checksum for the source PNG.
+
+Zenodo remains the archival release DOI:
+<https://doi.org/10.5281/zenodo.20132286>. This Hugging Face dataset is a
+discoverable and loadable mirror of the frozen evaluation split.
+
+## Load
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("shivamk3r/shape-code-bench-eval-v1")
+```
+
+## Columns
+
+- `image`: rendered target image.
+- `image_file_name`: original path under `data/eval_v1`.
+- `image_sha256`: checksum from `SHA256SUMS`.
+- `sample_id`, `split`, `difficulty`, `seed`: sample identity and generation seed.
+- `image_size`, `num_shapes`, `shape_inventory`: scene summary metadata.
+- `ground_truth_program`: canonical ShapeCodeBench DSL program.
+- `render_config`: deterministic V1 renderer configuration.
+
+## Evaluation Hygiene
+
+`eval_v1` is a frozen reporting split. Do not tune prompts, adapters, model
+checkpoints, heuristic parameters, or generator settings on this split and then
+report the result as clean held-out performance.
+
+For development, generate separate train/dev splits from fresh seeds using the
+ShapeCodeBench repository.
+
+## Links
+
+- Paper: <https://arxiv.org/abs/2605.11680>
+- GitHub: <https://github.com/shivamk3r/shape-code-bench>
+- Zenodo archived release: <https://doi.org/10.5281/zenodo.20132286>
+- Artifact license: <https://github.com/shivamk3r/shape-code-bench/blob/main/LICENSE-ARTIFACTS.md>
+
+## License
+
+The generated benchmark dataset is licensed under CC BY 4.0. ShapeCodeBench
+source code is licensed separately under MIT.