Duplicate from mmscale/mmscale-data

Co-authored-by: mmscale-neurips26 <mmscale@users.noreply.huggingface.co>

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+size_categories:
+- 10K<n<100K
+tags:
+- multimodal
+- moral-reasoning
+- vision-language
+- benchmark
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test.parquet
+---
+
+# MM-SCALE — multimodal moral-acceptability evaluation set
+
+**21,977 (image, scenario) pairs** across **8,444 DALL·E-generated images**, each
+labeled with a single `mean_rating ∈ [1, 5]` for moral acceptability and a
+single `modality_label ∈ {text, image, both}` indicating which modality the
+judgment hinges on.
+
+This is a **simplified eval view** — one row per (image, scenario) pair with one
+rating and one modality label. The underlying paired-annotation source (per-
+annotator ratings + modality votes, mixing humans and a GPT-5.2 oracle) is
+documented in the accompanying paper.
+
+## Schema (per row)
+
+| field | type | description |
+|---|---|---|
+| `image_id` | string | image identifier (matches the file in `images.tar.gz`) |
+| `image_filename` | string | basename of the image, e.g. `00010.jpg` |
+| `scenario_id` | string | unique scenario id, e.g. `00010.png_s01` |
+| `target_setting` | string | original caption / situation description |
+| `text` | string | the action being rated |
+| `mean_rating` | float | mean of all annotator ratings, on the 1–5 scale (1=clearly unacceptable, 5=clearly acceptable) |
+| `modality_label` | string | `"text"`, `"image"`, or `"both"` — which modality grounds the judgment |
+| `n_annotations` | int | number of paired (rating, modality) annotations underlying the row (1, 2, or 3) |
+
+## How `mean_rating` is derived
+
+For each scenario, available annotations include (a) original human annotators
+from MM-SCALE and (b) GPT-5.2 oracle annotations that filled missing slots up
+to 3 paired (rating, modality_vote) entries. `mean_rating` is the arithmetic
+mean of the `n_annotations` ratings.
+
+## How `modality_label` is derived
+
+1. **Singleton or unanimous human votes** → use the human label.
+2. **Human votes disagree** → pool human + oracle votes and take the overall
+   majority.
+3. **Still tied** (rare) → fall back to the first human vote.
+
+Coverage of the resolution paths in this release:
+
+- 15,040 rows: singleton human vote
+- 4,730 rows: ≥2 humans agreed
+- 2,207 rows: humans disagreed; resolved with the oracle
+
+## Distributions
+
+| `mean_rating` (rounded) | count | share |
+|---|---|---|
+| 1 | 1,118 | 5.1% |
+| 2 | 3,038 | 13.8% |
+| 3 | 4,893 | 22.3% |
+| 4 | 8,341 | 38.0% |
+| 5 | 4,587 | 20.9% |
+
+| `modality_label` | count | share |
+|---|---|---|
+| image | 11,055 | 50.3% |
+| both | 6,099 | 27.8% |
+| text | 4,823 | 21.9% |
+
+| `n_annotations` | count |
+|---|---|
+| 1 | 8,599 |
+| 2 | 4,122 |
+| 3 | 9,256 |
+
+## Images
+
+Images ship as a separate archive `images.tar.gz` (full-resolution JPEG, q=90,
+no downsizing) at this same repo. Reference each row's `image_filename` field
+to locate the file under `images/<image_filename>` after extraction.
+
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download("mmscale/mmscale-data", "images.tar.gz", repo_type="dataset")
+```
+
+## Loading
+
+```python
+from datasets import load_dataset
+ds = load_dataset("mmscale/mmscale-data")
+print(ds["test"][0])
+# {'image_id': '00010.png',
+#  'image_filename': '00010.jpg',
+#  'scenario_id': '00010.png_s01',
+#  'target_setting': '...',
+#  'text': '...',
+#  'mean_rating': 3.6667,
+#  'modality_label': 'image',
+#  'n_annotations': 3}
+```
+
+## Caveats
+
+- Images are synthetic (DALL·E); transfer to natural images is not validated
+  except on a small held-out SMID subset.
+- English-only.
+- 9,256 of 21,977 rows have `n_annotations == 3` (full paired coverage); the
+  rest (1 or 2 annotations) carry less-stable means. Filter on `n_annotations`
+  if your application needs fully-attested rows only.
+- Oracle modality votes follow a stricter "decisiveness" reading than humans
+  appear to use; pooled-majority resolution mitigates but does not eliminate
+  this in the 2,207 tiebreak rows.
+
+## License
+
+This dataset is released under the
+**Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
+License** (CC-BY-NC-SA 4.0):
+https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+
+The license is inherited from **Commonsense Norm Bank**, the upstream source
+of the moral situations used to author scenarios in MM-SCALE. Per the upstream
+terms:
+
+> Commonsense Norm Bank is licensed under the Creative Commons
+> Attribution-NonCommercial-ShareAlike 4.0 International License
+> (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). By requesting
+> access to the dataset, you agree to follow the terms outlined in the
+> license.
+
+By using MM-SCALE you agree to the same terms (attribution, non-commercial
+use, share-alike).
+
+### Accessing the original Commonsense Norm Bank
+
+If you need the original Commonsense Norm Bank corpus (e.g. to reproduce
+scenario authoring upstream of MM-SCALE), submit an access request here:
+
+https://docs.google.com/forms/d/e/1FAIpQLSfk47SwWW4pUay4TMSFO7IaG2vxwpbUPI-TJIZwhwdBDNGqoQ/viewform
+
+MM-SCALE itself does **not** redistribute the unmodified Commonsense Norm Bank
+corpus; only the scenarios authored on top of those moral situations together
+with their image pairings and annotations.
+
+## Citation
+
+```bibtex
+@inproceedings{mmscale,
+  title={MM-SCALE: ...},
+  author={...},
+  year={2026}
+}
+```

images.tar.gz

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