Datasets:

remake123
/

CWBench

@@ -1,101 +0,0 @@
----
-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 student view (half width/height of the original) |
-| `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.