re-upload corrected README.md

README.md

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+---
+license: other
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- multimodal
+- vision-language
+- on-policy-distillation
+- dual-world
+- benchmark
+size_categories:
+- 1K<n<10K
+---
+
+# CWBench
+
+A **dual-world** VQA benchmark and training set for cross-world aligned
+distillation (CWAD) of vision-language models.
+
+Each item is a *pair*: **world A** is an original image, **world B** is a
+counterfactual edit of that same image that flips the answer to the question.
+A model is scored on both members; a pair counts as correct only when **both**
+worlds are answered correctly (cross-world pair accuracy, CWPA).
+
+## Files
+
+```
+train.jsonl    2365 rows   training set (dual-world)
+test.jsonl     1188 rows   test set = 594 pairs × {A, B}
+train/         4730 images referenced by train.jsonl
+test/          1188 images referenced by test.jsonl
+```
+
+Image paths inside the jsonl are relative to this directory, so the tree is
+self-contained.
+
+## Schemas
+
+### `train.jsonl` — one object per training row
+
+| field | meaning |
+|---|---|
+| `prompt` | chat messages, `<image>` marks where the image goes |
+| `images` | **world A** — the original image at teacher resolution, matching `test.jsonl` member A |
+| `teacher_images` | **world B** — the counterfactual edit at 1024×1024 |
+| `reward_model.ground_truth` | letter answer for the row |
+| `ability`, `data_source`, `extra_info` | provenance metadata carried over from the source release |
+
+### `test.jsonl` — one object per *member* (two rows per pair, same `pair_id`)
+
+| field | meaning |
+|---|---|
+| `pair_id` | join key; `A` and `B` rows share it |
+| `member` | `"A"` (original) or `"B"` (edited) |
+| `images` | single-element list with the image path |
+| `query` | the question, with options |
+| `response` | ground-truth letter |
+| `category` | edit category tag |
+| `world_a_resolution` | resolution mode used for world A |
+
+## Loading
+
+```python
+import json
+
+rows = [json.loads(l) for l in open("train.jsonl", encoding="utf-8")]
+row = rows[0]
+print(row["prompt"][0]["content"])
+print(row["images"][0]["image"], row["teacher_images"][0]["image"])
+
+# score a submission pair-wise
+test = [json.loads(l) for l in open("test.jsonl", encoding="utf-8")]
+pairs = {}
+for e in test:
+    pairs.setdefault(e["pair_id"], {})[e["member"]] = e
+```
+
+`test.jsonl` is not in a format `datasets.load_dataset` can auto-convert (the
+images are plain files, not embedded bytes). Read the jsonl and open the images
+yourself.
+
+## Provenance and licence
+
+This release is derived from **RP-OPSD Dataset4.0**, whose questions originate
+from public VQA sources (e.g. A-OKVQA). The images are redistributed as part of
+that derived release. **Check that the upstream licences permit redistribution
+before publishing or reusing this dataset.** The `license` field above is set to
+`other` because the upstream terms, not an SPDX identifier, govern it.
+
+## Splits
+
+`train.jsonl` and `test.jsonl` are disjoint:
+
+- **0** image files in common (checked across all four A/B combinations)
+- **0** question strings in common
+
+The `extra_info.dataset4_source_index` in `train.jsonl` and `source_idx` in
+`test.jsonl` come from **different index namespaces** and must not be joined
+against each other.