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README.md

@@ -5,118 +5,22 @@ task_categories:
 pretty_name: RISEBench
 size_categories:
 - n<1K
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: subset
-    dtype: string
-  - name: prompt
-    dtype: string
-  - name: source_image
-    dtype: image
-  - name: reference_image
-    dtype: image
-  - name: reference
-    dtype: string
-  - name: reference_txt
-    dtype: string
-  - name: consistency_free
-    dtype: bool
-  - name: reasoning_img
-    dtype: bool
-  - name: reasoning_wo_ins
-    dtype: bool
-  splits:
-  - name: test
-    num_bytes: 283754127
-    num_examples: 360
-  download_size: 283737698
-  dataset_size: 283754127
 ---
 
 # RISEBench
 
-This is a normalized Hugging Face version of the official RISEBench benchmark data, prepared for direct benchmark consumption in `T2I-Eval`.
-
-## Source
-
-- Official dataset repo: `PhoenixZ/RISEBench`
-- Official code repo: `VisionXLab/RISEBench`
-- Source artifacts used here:
-  - `datav2_total_w_subtask.json`
-  - `data.zip`
-
-## What is included
-
-This repo contains the official full benchmark input images and metadata reorganized into one `test` split with a stable schema.
-
-Category counts:
-
-- `temporal_reasoning`: 85 rows
-- `causal_reasoning`: 90 rows
-- `spatial_reasoning`: 100 rows
-- `logical_reasoning`: 85 rows
-
-Total rows: `360`
-
-## Normalization choices
-
-- Unified the benchmark into a single `test` split.
-- Stored benchmark routing fields as `suite=risebench` and `subset=<category>`.
-- Preserved the official benchmark id in `sample_id`.
-- Stored source images in `source_image` as Hugging Face `Image` features.
-- Stored optional reference images in `reference_image` when provided by the official data.
-- Preserved the official instruction as both `prompt` and `turn_prompts=[prompt]` for compatibility with the shared image-edit runtime.
-- Preserved official evaluation hints such as `consistency_free`, `reasoning_img`, `reasoning_wo_ins`, and `subtask`.
+Minimized normalized benchmark dataset for `T2I-Eval`.
 
 ## Schema
 
-Main columns:
-
-- `sample_id`: official benchmark sample id
-- `suite`: constant `risebench`
-- `subset`: category token used for output grouping
-- `category`: reasoning category
-- `subtask`: official fine-grained task label
-- `task_kind`: constant `single_turn`
-- `prompt`: edit instruction
-- `turn_prompts`: list containing the prompt
-- `num_turns`: always `1`
-- `source_image_relpath`: original relative source path
-- `source_image`: source image
-- `reference_image_relpath`: optional relative reference image path
-- `reference_image`: optional reference image
-- `reference`: official reference description text
-- `reference_txt`: optional official text answer for logical tasks
-- `reference_old`: optional legacy reference description
-- `problem`: optional official problem marker
-- `consistency_free`: whether appearance-consistency scoring is skipped
-- `reasoning_img`: whether the official reasoning judge uses an additional image input
-- `reasoning_img_op`: optional official flag preserved from source
-- `reasoning_wo_ins`: whether the official logical judge omits the instruction
-- `eval_protocol`: normalized evaluation protocol label
-- `source_repo`: upstream dataset repo id
-- `source_metadata_file`: upstream metadata filename
-- `source_archive_member`: original member path inside `data.zip`
-- `reference_archive_member`: original reference-image member path inside `data.zip`
-
-## Usage
+- `subset`: reasoning category used for benchmark routing
+- `prompt`: editing instruction
+- `source_image`: input image
+- `reference_image`: optional reference answer image for some tasks
+- `reference`: optional text reference description
+- `reference_txt`: optional text answer for logical tasks
+- `consistency_free`: whether consistency judging is skipped
+- `reasoning_img`: whether reasoning judging also uses the input image
+- `reasoning_wo_ins`: whether logical reasoning judging omits the instruction
 
-```python
-from datasets import load_dataset
-
-ds = load_dataset("Jialuo21/RISEBench", split="test")
-print(ds[0]["sample_id"], ds[0]["category"], ds[0]["prompt"])
-```
-
-Example access pattern:
-
-```python
-row = ds[0]
-source = row["source_image"]
-reference = row["reference_image"]
-```
+Total rows: `360`