Add SemVarBench README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-to-image
+- text-generation
+language:
+- en
+tags:
+- compositionality
+- semantic-variation
+- text-to-image
+- benchmark
+- vision-language
+pretty_name: SemVarBench
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: test
+    path: test.parquet
+---
+
+# SemVarBench
+
+SemVarBench is the benchmark from the ICLR 2025 paper
+[**Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective**](https://openreview.net/forum?id=NWb128pSCb),
+designed together with the **SemVarEffect** metric to evaluate the causality between
+semantic variations in the input text and the generated image in text-to-image (T2I)
+synthesis.
+
+Each example is built around a base caption `T0` and a minimally-edited variant `T1`
+that changes the composition (e.g. swapped subject/object or swapped attributes) while
+reusing the same words, plus `T2`, a paraphrase of `T1` (passive voice / reordering)
+that is semantically equivalent to `T1`. Semantic variations are achieved through two
+types of linguistic permutations while avoiding easily predictable literal variations.
+
+This dataset is the flattened version of the `benchmark/` directory in the
+[SemVarBench GitHub repository](https://github.com/zhuxiangru/SemVarBench), merging:
+
+- [`trainingset/training_data.txt`](https://github.com/zhuxiangru/SemVarBench/blob/main/benchmark/trainingset/training_data.txt) — the **train** split (10,770 rows).
+- [`testset/test_data.txt`](https://github.com/zhuxiangru/SemVarBench/blob/main/benchmark/testset/test_data.txt) — the **test** split (684 rows).
+- [`testset_divided_category/`](https://github.com/zhuxiangru/SemVarBench/tree/main/benchmark/testset_divided_category) — 20 per-category slices of the test set, used here to populate the `categories` field.
+
+## Data fields
+
+| Column | Description |
+|--------|-------------|
+| `id` | Unique example identifier (e.g. `0_61_326`). |
+| `T0` | Base caption. |
+| `T1` | Semantically varied caption (minimal compositional edit of `T0`). |
+| `T2` | Paraphrase of `T1` (passive / reordered), semantically equivalent to `T1`. |
+| `categories` | List of contrast-category tags. Populated for the **test** split; empty (`[]`) for the **train** split. A test example may carry more than one tag (118 of 684 do). |
+
+### The 20 test categories
+
+`absolute_location`, `action`, `age`, `appearance`, `color`, `counting`, `direction`,
+`height`, `interaction`, `manner`, `material`, `relative_location`, `sentiment`,
+`shape`, `size`, `spatio_temporal`, `temperature`, `texture`, `vague_amount`, `weight`.
+
+## Splits
+
+| Split | Rows |
+|-------|------|
+| `train` | 10,770 |
+| `test`  | 684 |
+
+Train and test ids are disjoint. The 20 category files together cover exactly the 684
+test ids (no more, no fewer).
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("zhuxiangru/SemVarBench")
+print(ds["test"][0]["T0"], "||", ds["test"][0]["T1"], "||", ds["test"][0]["T2"])
+
+# filter the test set to a single category
+color = ds["test"].filter(lambda r: "color" in r["categories"])
+print(len(color), "color examples")
+```
+
+## Related work
+
+The predecessor dataset [Winoground-T2I](https://github.com/zhuxiangru/Winoground-T2I)
+is also available on the Hub at
+[`zhuxiangru/Winoground-T2I`](https://huggingface.co/datasets/zhuxiangru/Winoground-T2I).
+
+## Citation
+
+If you find the data in our project useful, please consider citing our work:
+
+```bibtex
+@inproceedings{DBLP:conf/iclr/ZhuSSXL00YX25,
+  author       = {Xiangru Zhu and
+                  Penglei Sun and
+                  Yaoxian Song and
+                  Yanghua Xiao and
+                  Zhixu Li and
+                  Chengyu Wang and
+                  Jun Huang and
+                  Bei Yang and
+                  Xiaoxiao Xu},
+  title        = {Evaluating Semantic Variation in Text-to-Image Synthesis: {A} Causal
+                  Perspective},
+  booktitle    = {The Thirteenth International Conference on Learning Representations,
+                  {ICLR} 2025, Singapore, April 24-28, 2025},
+  publisher    = {OpenReview.net},
+  year         = {2025},
+  url          = {https://openreview.net/forum?id=NWb128pSCb},
+  timestamp    = {Thu, 15 May 2025 17:19:05 +0200},
+  biburl       = {https://dblp.org/rec/conf/iclr/ZhuSSXL00YX25.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```