Update dataset card

README.md

@@ -1,3 +1,135 @@
----
-license: mit
----
+---
+pretty_name: OmniClean
+language:
+- en
+- zh
+multilinguality: multilingual
+license: other
+task_categories:
+- question-answering
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: slim
+  data_files:
+  - split: test
+    path: omniclean.test.jsonl
+---
+
+# OmniClean
+
+OmniClean is a leakage-aware omni-modal evaluation set built from retained examples across 9 source benchmarks. It is designed to reduce visual-shortcut effects in omni evaluation by applying visual-only probing where query-level filtering is defined, while keeping selected full subsets for protocol-exception benchmarks where a filtered subset is undefined or intentionally not reported.
+
+This release contains **8,551** evaluation examples in a minimal `slim` JSONL format.
+
+## What this release is
+
+Raw omni benchmark scores can be inflated by visually answerable examples. OmniClean is intended to provide a cleaner evaluation target for audio-visual-language QA and related omni understanding tasks.
+
+This release is for evaluation. It is not intended as a training corpus.
+
+## Composition
+
+Total examples: **8,551**
+
+| Source benchmark (`dataset_source`) | Examples | Notes |
+|---|---:|---|
+| `AV_Odyssey_Bench` | 4555 | Full selected subset retained as a protocol exception |
+| `VideoHolmes` | 885 | Query-level cleaned subset |
+| `WorldSense` | 875 | Query-level cleaned subset |
+| `IntentBench` | 660 | Query-level cleaned subset |
+| `OmniBench` | 417 | Query-level cleaned subset |
+| `CG-AV-Counting` | 376 | Full selected subset retained as a protocol exception |
+| `OmniVideoBench` | 318 | Query-level cleaned subset |
+| `Daily-Omni` | 237 | Query-level cleaned subset |
+| `UNO-Bench` | 228 | Query-level cleaned subset |
+
+## Data format
+
+Each record contains the following fields:
+
+- `dataset_source`: source benchmark name
+- `source_id`: source sample identifier
+- `question`: question text
+- `options`: candidate answers; may be empty for some benchmarks
+- `answer`: benchmark-native gold answer
+- `media_paths`: relative media references with `image`, `audio`, and `video` lists
+- `question_type`: benchmark-native question category; may be `null`
+
+Example:
+
+```json
+{
+  "dataset_source": "OmniVideoBench",
+  "source_id": "omnivideobench:0",
+  "question": "Before picking up the kitten, the blogger explains a sign. Which concepts can it be associated with?",
+  "options": [
+    "A.Ancient Chinese stories and Japanese anime",
+    "B.Ancient Chinese Imperial Palace Architecture and Japanese Bar Names",
+    "C.A certain type of Chinese cuisine and a certain type of Southeast Asian opera",
+    "D.Chinese garden art and Western palace architecture"
+  ],
+  "answer": "Ancient Chinese stories and Japanese anime",
+  "media_paths": {
+    "image": [],
+    "audio": [],
+    "video": ["videos/video_1.mp4"]
+  },
+  "question_type": "reference reasoning"
+}
+```
+
+## Important notes
+
+### Benchmark-native answers
+`answer` is not normalized into a single format across all sources. Depending on the benchmark, it may be:
+
+- a single option letter such as `A`
+- multiple option letters such as `D,E,F`
+- a numeric answer such as `18`
+- the full answer text
+- a short free-form label such as `Yes`
+
+Evaluation should therefore use benchmark-aware answer normalization.
+
+### Optional fields by source benchmark
+- `options` can be empty for some examples.
+- `question_type` can be `null` for some examples.
+- `media_paths` always contains the keys `image`, `audio`, and `video`, but some lists are empty.
+
+### Protocol exceptions
+Two source benchmarks are intentionally retained as selected full subsets in this release:
+
+- `AV_Odyssey_Bench`: a visual-only filtered subset is not defined because some answer options contain audio-bearing content.
+- `CG-AV-Counting`: visual-only probing is used diagnostically, but a filtered-score benchmark is not reported because further exclusion would overly shrink an already difficult subset.
+
+## Loading with `datasets`
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("che111/OmniClean", "slim", split="test")
+print(ds[0])
+```
+
+## Limitations
+
+- This release keeps benchmark-native answer formats instead of forcing a single unified answer schema.
+- Source benchmarks differ in modality structure: some examples are video-only, some are image+audio, and some are audio+video.
+- Relative paths in `media_paths` should be interpreted with respect to the released data layout.
+
+## Citation
+
+If you use OmniClean, please cite the accompanying paper:
+
+```bibtex
+@misc{omniclean2026,
+  title={Probing and Boosting Omni Understanding: Leakage-Aware Evaluation and a Staged Post-Training Study},
+  author={StepFun-Audio Team},
+  year={2026}
+}
+```
+
+## License
+
+Please replace this section with the final license and confirm that redistribution terms are compatible with all included source benchmarks and media assets.