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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- visual-question-answering
+- multiple-choice
+language:
+- en
+pretty_name: SAT (Spatial Aptitude Test)
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*.parquet
+---
+
+# SAT (Spatial Aptitude Test) — re-hosted for lmms-eval
+
+This is a re-hosted copy of [`array/SAT`](https://huggingface.co/datasets/array/SAT)
+prepared for upstream-friendly use with
+[`EvolvingLMMs-Lab/lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval).
+
+## What changed vs. `array/SAT`
+
+The original parquet uses a nested `list<binary>` schema for image bytes that
+fails pyarrow chunked-array conversion when `load_dataset` is called without
+`streaming=True`. lmms-eval's `api/task.py` calls `load_dataset` with
+`num_proc=1`, which is incompatible with streaming, so neither path works
+out-of-the-box.
+
+The re-host fixes this by:
+
+1. **Storing images as `Sequence(Image())`** instead of raw nested binary —
+   `datasets` handles the parquet encoding correctly and non-streaming loads
+   succeed.
+2. **Pre-shuffling the answer order** with a fixed seed (`random.Random(42)`)
+   and storing `correct_answer_idx` directly. The upstream fork's
+   `api/task.py` originally shuffled answer order at load time with a
+   non-deterministic seed; baking this in makes the eval reproducible and
+   removes the need for any framework patch.
+
+All other fields (`question`, `question_type`, `correct_answer`) are passed
+through unchanged.
+
+## Schema
+
+| Field                 | Type                       | Description                              |
+|-----------------------|----------------------------|------------------------------------------|
+| `image_bytes`         | `Sequence(Image())`        | 1 or 2 RGB JPEGs per item                |
+| `question`            | `Value("string")`          | Question text                            |
+| `answers`             | `Sequence(Value("string"))`| Two answer choices, **shuffled** seed=42 |
+| `correct_answer_idx`  | `Value("int32")`           | 0 or 1, index into `answers`             |
+| `correct_answer`      | `Value("string")`          | Full text of the correct answer          |
+| `question_type`       | `Value("string")`          | One of `obj_movement`, `ego_movement`, `action_conseq`, `perspective`, `goal_aim` |
+
+## Stats
+
+- 150 test items, single `test` split
+- Image count per item: 1 (104 items) or 2 (46 items)
+- Answer count per item: 2 (binary MCQ)
+- Question types: `action_conseq` 37, `goal_aim` 34, `perspective` 33,
+  `obj_movement` 23, `ego_movement` 23
+
+## License
+
+Inherits from the original `array/SAT` release. Use under the original
+licensing terms.
+
+## Citation
+
+```bibtex
+@article{ray2025sat,
+  title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
+  author={Ray, Arijit and Duan, Jiafei and Tan, Reuben and Bashkirova, Dina and Hendrix, Rose and Ehsani, Kiana and Kembhavi, Aniruddha and Plummer, Bryan A and Krishna, Ranjay and Zeng, Kuo-Hao and others},
+  journal={arXiv preprint arXiv:2412.07755},
+  year={2025}
+}
+```