Add dataset files and card

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+questions.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2024 Arijit Ray
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+pretty_name: SAT train split, dynamic spatial questions
+language:
+- en
+task_categories:
+- visual-question-answering
+tags:
+- spatial-reasoning
+- multi-image
+- synthetic
+size_categories:
+- 10K<n<100K
+---
+
+# SAT train split: dynamic spatial questions
+
+A re-packaged subset of the **train split** of SAT (Spatial Aptitude Training, Ray et al.). It holds the 41,619 dynamic questions of `SAT_train.parquet` with their 58,919 images, extracted from the parquet file into image files. Questions, answers and images are unchanged; nothing is added.
+
+## Source
+
+- Dataset: [array/SAT](https://huggingface.co/datasets/array/SAT), file `SAT_train.parquet`, revision `bda5dde942d7f7b41bff7935f086ed9a9e348ae3`
+- Project page: https://arijitray1993.github.io/SAT/
+- Paper: https://arxiv.org/abs/2412.07755
+- The images are synthetic renders of procedurally generated indoor scenes (ProcTHOR / AI2-THOR).
+
+## Contents
+
+| Path | Content |
+|---|---|
+| `questions.jsonl` | 41,619 rows, one per question |
+| `images-000.tar`, `images-001.tar` | 58,919 images (56,465 JPEG, 2,454 PNG), member paths `images/<row>_<k>.<jpg or png>` (uncompressed tar) |
+| `files.tsv` | every image: path, bytes, sha256, tar file |
+| `LICENSE` | the MIT license text |
+
+Fields of `questions.jsonl`:
+
+| Field | Meaning |
+|---|---|
+| `id` | `sat-train-<row>` |
+| `row` | 0-based row index in `SAT_train.parquet` at the revision above |
+| `question_type` | `action_consequence` (15,100), `action_sequence` (10,370), `obj_movement` (6,930), `goal_aim` (6,765), `perspective` (2,454) |
+| `question` | upstream `question`, unchanged |
+| `answers` | upstream `answers` (the answer choices), unchanged and in upstream order |
+| `correct_answer` | upstream `correct_answer`, unchanged |
+| `images` | paths of the row's images (1 or 2), in upstream order |
+
+The upstream card asks users to randomize the order of the answer choices when presenting them to a model; this copy keeps the upstream order.
+
+## Processing
+
+1. Downloaded `SAT_train.parquet` at the pinned revision.
+2. Kept the rows whose `question_type` is one of the five dynamic types above: 41,619 of the 172,384 rows. The 130,765 rows of type `other` (static questions) are left out.
+3. Wrote each element of `image_bytes` to `images/<row>_<k>.<ext>` (`<row>` zero-padded to 6 digits, `<k>` the position in the list, `<ext>` from the image's format). The bytes are the upstream bytes: no re-encoding, resizing or filtering. Identical images that upstream stores under different rows stay separate files.
+4. Copied `question`, `answers`, `question_type` and `correct_answer` unchanged, and added `id`, `row` and `images`.
+5. Packed the images, sorted by path, into two uncompressed tar files of at most 1 GB each.
+
+No question or answer was edited, generated or filtered by a model.
+
+## Split
+
+Training data only: every row comes from the SAT train split (`SAT_train.parquet`). Nothing from `SAT_static.parquet`, `SAT_val.parquet` or the real-image test split (`SAT_test.parquet`) is included.
+
+## License
+
+MIT, the license declared by the upstream dataset card ([array/SAT](https://huggingface.co/datasets/array/SAT)). The `LICENSE` file carries the MIT text with the copyright notice of the SAT project (`Copyright (c) 2024 Arijit Ray`, from [github.com/arijitray1993/SAT](https://github.com/arijitray1993/SAT/blob/bd625b3723e13028f0cb914a09e1bb0992505186/LICENSE)). This copy is distributed under the same license.
+
+## Attribution and citation
+
+The questions, answers and images are the work of the SAT authors. If you use this data, please cite their paper:
+
+```bibtex
+@misc{ray2025satdynamicspatialaptitude,
+      title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
+      author={Arijit Ray and Jiafei Duan and Ellis Brown and Reuben Tan and Dina Bashkirova and Rose Hendrix and Kiana Ehsani and Aniruddha Kembhavi and Bryan A. Plummer and Ranjay Krishna and Kuo-Hao Zeng and Kate Saenko},
+      year={2025},
+      eprint={2412.07755},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2412.07755},
+}
+```

images-000.tar

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+size 1033410560

images-001.tar

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+oid sha256:e8f8f7c2a8caadb865e8bca358aca5c1c684949259258058df5f36b9c4d161a4
+size 897699840

questions.jsonl

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