Clean up: drop legacy missing_tables_old folders, add dataset card

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README.md

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+---
+pretty_name: MoRA Missing-Modality Benchmark Data
+license: other
+language:
+- en
+task_categories:
+- image-classification
+- text-classification
+tags:
+- multimodal
+- missing-modality
+- vision-language
+- clip
+- mm-imdb
+- food101
+- hateful-memes
+size_categories:
+- 10K<n<100K
+viewer: false
+---
+
+# MoRA — Missing-Modality Benchmark Data
+
+Preprocessed, ready-to-train data for reproducing **MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition**.
+
+- 📄 Paper: [arXiv:2511.06225](https://arxiv.org/abs/2511.06225)
+- 💻 Code: [github.com/Tree-Shu-Zhao/MoRA](https://github.com/Tree-Shu-Zhao/MoRA)
+
+This bundle exists so you don't have to redo the (error-prone) raw download + preprocessing. The Arrow files and JSON metadata drop **directly** into the repo's `datasets/` directory with no renaming — filenames already match what `src/data/dataset.py` expects.
+
+## Quick start
+
+```bash
+# from the MoRA repo root
+huggingface-cli download TreezzZ/MoRA-data --repo-type dataset --local-dir datasets
+# then, per the README:
+python src/main.py experiment=mora_mmimdb
+python src/main.py experiment=mora_hatememes
+python src/main.py experiment=mora_food101
+```
+
+That's it — `DATA_DIR` defaults to `datasets`, so the three dataset folders line up automatically.
+
+## Directory structure
+
+```
+.
+├── Food101/
+│   ├── train.arrow            # image bytes + text + label
+│   ├── val.arrow
+│   ├── test.arrow
+│   ├── class_idx.json         # 101 class → index map
+│   ├── text.json              # per-sample UPMC text titles
+│   ├── split.json             # train/val/test sample ids
+│   └── missing_tables/        # precomputed missing-modality masks (see below)
+├── hateful_memes/
+│   ├── train.arrow
+│   ├── val.arrow              # = dev split
+│   ├── test.arrow             # labelled test (from test_seen/test_unseen)
+│   ├── test_unseen.jsonl
+│   └── missing_tables/
+└── mmimdb/
+    ├── train.arrow
+    ├── val.arrow              # = dev split
+    ├── test.arrow
+    ├── split.json
+    └── missing_tables/
+```
+
+### Arrow schema
+Each `*.arrow` is a PyArrow IPC file. Columns:
+- **MM-IMDb**: `image` (JPEG bytes), `plots` (list[str]), `label` (23-d multi-hot), `genres`, `image_id`, `split`
+- **Hateful Memes**: `image` (PNG bytes), `text` (list[str]), `label` (int), `split`
+- **Food-101**: `image` (JPEG bytes), `text` (list[str]), `label` (int), `image_id`, `split`
+
+### `missing_tables/`
+Precomputed missing-modality masks used by the paper. Naming convention (matches `src/data/dataset.py`):
+
+```
+{dataset}_split{split}_missing_{type}_{ratio}.pt
+  dataset ∈ {food101, hatememes, mmimdb}
+  split   ∈ {train, val, test}
+  type    ∈ {both, image, text}
+  ratio   ∈ {0.1, 0.2, …, 0.9}
+```
+
+Each file is a 1-D `torch` tensor of length *N* (number of samples): `0` = both modalities present, `1` = text missing, `2` = image missing. When a file matching the active `missing_params` exists at `datasets/<DS>/missing_tables/`, the dataloader **loads it**; otherwise it regenerates one deterministically from `SEED`. Shipping these guarantees everyone evaluates on identical masks.
+
+## Reproduction
+
+The repo's default config is **missing type `both`, ratio `0.7`** (`BOTH_RATIO=0.5`). Using this bundle's masks, a single H100 run per dataset reproduces the paper's Table 1 (70%, "both") within run-to-run noise:
+
+| Dataset | Metric | Paper | Reproduced |
+|---|---|---|---|
+| MM-IMDb | F1-macro | 52.97 | 52.53 |
+| Hateful Memes | AUROC | 70.15 | 69.56 |
+| Food-101 | Accuracy | 83.77 | 83.30 |
+
+(Residual ≤0.6 pt is fp16/cuDNN training nondeterminism; masks are identical.)
+
+## Data provenance & licensing
+
+This is a **research convenience redistribution** of three public benchmarks. The original data are owned by their respective creators and remain under their original terms — **you are responsible for complying with each source's license/usage agreement**:
+
+- **MM-IMDb** — [gmu-mmimdb](https://github.com/johnarevalo/gmu-mmimdb) / [archive.org mirror](https://archive.org/download/mmimdb)
+- **UPMC Food-101** — [VISIIR](https://visiir.isir.upmc.fr/explore) / [Kaggle](https://www.kaggle.com/datasets/gianmarco96/upmcfood101)
+- **Hateful Memes** — [Meta AI](https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set/) (subject to the Hateful Memes Dataset License Agreement; redistribution of the original images may be restricted — use accordingly)
+
+If you are a rights holder and have concerns about redistribution, please contact the repository owner.
+
+## Citation
+
+```bibtex
+@article{zhao2025mora,
+  title={MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition},
+  author={Zhao, Shu and Ahuja, Nilesh and Yu, Tan and Shen, Tianyi and Narayanan, Vijaykrishnan},
+  journal={arXiv preprint arXiv:2511.06225},
+  year={2025}
+}
+```

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