Update dataset card for 10 percent test subset

README.md

@@ -15,46 +15,13 @@ tags:
 license: other
 size_categories:
 - 1K<n<10K
-dataset_info:
-  features:
-  - name: index
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: question
-    dtype: string
-  - name: question_type
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: answer_type
-    dtype: string
-  - name: category
-    dtype: string
-  - name: split
-    dtype: string
-  - name: real_image_path
-    dtype: string
-  - name: origin_dataset
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2041995103
-    num_examples: 8657
-  download_size: 1991106611
-  dataset_size: 2041995103
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
 ---
 
 # HVSBench
 
 HVSBench is a benchmark for evaluating how well multimodal large language models align with human perceptual behavior. It covers human visual system tasks across prominence, subitizing, prioritizing, free-viewing, and searching.
 
-This Hugging Face release packages the cleaned subset provided with the repository as parquet shards with embedded image bytes. It contains 8,657 question-answer examples and 7,507 unique raw images referenced through the `image` column. The corresponding paper describes the full HVSBench benchmark with 85,147 multimodal QA pairs across 13 question types and 5 fields.
+This Hugging Face release packages the divided 10% test subset described in the paper as parquet shards with embedded image bytes. It contains 8,657 question-answer examples and 7,507 unique raw images referenced through the `image` column. The corresponding paper describes the full HVSBench benchmark with 85,147 multimodal QA pairs across 13 question types and 5 fields.
 
 Paper: **Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior**
 
@@ -70,13 +37,13 @@ Columns:
 
 - `image`: raw RGB image embedded in parquet
 - `index`: source row index
-- `real_image_path`: original relative path under `raw_datasets`
 - `question`: benchmark prompt
 - `question_type`: question type ID, Q1-Q13
 - `answer`: ground-truth answer
 - `answer_type`: answer format, such as `single_choice`, `int_number`, `sorting`, or `fixation_prediction`
 - `category`: benchmark category, such as `saliency_rank`, `salient_instance`, or `scanpath`
 - `split`: original split label
+- `real_image_path`: original relative path under `raw_datasets`
 - `origin_dataset`: source dataset name
 
 ## Data Statistics