Add Moving MNIST test split as Hugging Face dataset

README.md

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+---
+pretty_name: Moving MNIST (test split)
+task_categories:
+- video-classification
+language:
+- en
+tags:
+- moving-mnist
+- video
+- synthetic
+- grayscale
+size_categories:
+- 10K<n<100K
+---
+
+# Moving MNIST (test split)
+
+This dataset is the classic **Moving MNIST** benchmark test set released by Nitish Srivastava et al. for sequence prediction and video representation learning.
+
+- Source file: `mnist_test_seq.npy`
+- Original source: https://www.cs.toronto.edu/~nitish/unsupervised_video/
+- Content: 10,000 sequences, each 20 frames long
+- Frame size: 64x64, grayscale
+- Data type: `uint8`
+
+## Dataset structure
+
+This Hugging Face dataset stores one sequence per row:
+
+- Split: `test`
+- Number of rows: `10000`
+- Feature schema:
+  - `video`: `Array3D(shape=(20, 64, 64), dtype='uint8')`
+
+Each `video` item is a full sequence of 20 frames.
+
+## How the original data was created
+
+The original Moving MNIST sequences are synthetic videos formed by placing MNIST digit sprites into a 64x64 canvas and moving them with constant velocity and elastic wall bounces. In this specific benchmark file, each sequence contains **two moving digits** over 20 time steps.
+
+The released benchmark file (`mnist_test_seq.npy`) is arranged as:
+
+- Raw shape: `(20, 10000, 64, 64)` = `(time, sequence, height, width)`
+
+For this Hugging Face conversion, it is reorganized conceptually into per-example rows:
+
+- Per-example shape: `(20, 64, 64)`
+
+## Citation
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@inproceedings{srivastava2015unsupervised,
+  title={Unsupervised Learning of Video Representations using LSTMs},
+  author={Srivastava, Nitish and Mansimov, Elman and Salakhutdinov, Ruslan},
+  booktitle={International Conference on Machine Learning (ICML)},
+  year={2015}
+}
+```
+
+And optionally reference the project page distributing the benchmark file:
+
+- https://www.cs.toronto.edu/~nitish/unsupervised_video/

dataset_dict.json

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+{"splits": ["test"]}

test/data-00000-of-00002.arrow

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test/dataset_info.json

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+{
+  "builder_name": "generator",
+  "citation": "",
+  "config_name": "default",
+  "dataset_name": "generator",
+  "dataset_size": 871240000,
+  "description": "",
+  "download_size": 0,
+  "features": {
+    "video": {
+      "shape": [
+        20,
+        64,
+        64
+      ],
+      "dtype": "uint8",
+      "_type": "Array3D"
+    }
+  },
+  "homepage": "",
+  "license": "",
+  "size_in_bytes": 871240000,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 871240000,
+      "num_examples": 10000,
+      "shard_lengths": [
+        6000,
+        4000
+      ],
+      "dataset_name": "generator"
+    }
+  },
+  "version": {
+    "version_str": "0.0.0",
+    "major": 0,
+    "minor": 0,
+    "patch": 0
+  }
+}

test/state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00002.arrow"
+    },
+    {
+      "filename": "data-00001-of-00002.arrow"
+    }
+  ],
+  "_fingerprint": "4b1a3b9c6545886a",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}