Datasets:

ohjoonhee
/

vstar_bench

dataset_info:
  - config_name: default
    features:
      - name: image
        dtype: image
      - name: category
        dtype: string
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: choices
        list: string
      - name: answer
        dtype: int64
    splits:
      - name: test
        num_bytes: 281192667
        num_examples: 191
    download_size: 281175185
    dataset_size: 281192667
  - config_name: direct_attributes
    features:
      - name: image
        dtype: image
      - name: category
        dtype: string
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: choices
        list: string
      - name: answer
        dtype: int64
    splits:
      - name: test
        num_bytes: 169304473
        num_examples: 115
    download_size: 168837509
    dataset_size: 169304473
  - config_name: relative_position
    features:
      - name: image
        dtype: image
      - name: category
        dtype: string
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: choices
        list: string
      - name: answer
        dtype: int64
    splits:
      - name: test
        num_bytes: 111888168
        num_examples: 76
    download_size: 112339908
    dataset_size: 111888168
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
  - config_name: direct_attributes
    data_files:
      - split: test
        path: direct_attributes/test-*
  - config_name: relative_position
    data_files:
      - split: test
        path: relative_position/test-*

vstar_bench (Reformatted)

This dataset is a reformatted version of craigwu/vstar_bench.

The underlying data (images, questions, choices, and answers) is unchanged. Only the representation has been normalized to make it easier to use in standard multiple-choice (MCQ) and instruction-following evaluation pipelines.

Dataset Format

Each example contains:

question (string): question text
choices (List[str]): answer options (no letter prefixes)
answer (int): correct option index (0-based)
image (Image): associated image

Reconstructing MCQ Prompt

The original letter-based MCQ format can be reconstructed as follows:

from datasets import load_dataset

ds = load_dataset("ohjoonhee/vstar_bench")
row = ds["test"][0]

question = row["question"]
choices = row["choices"]
answer = row["answer"]

post_prompt = "Answer with the option's letter from the given choices directly."

choices = [f"({chr(i + ord('A'))}) {choice}" for i, choice in enumerate(choices)]
text = "\n".join([question] + choices + [post_prompt])
print(text)

label = ["A", "B", "C", "D"][answer]
print(label)

Notes

Fully reversible to the original dataset format
No samples were modified, added, or removed

For the original dataset, see: craigwu/vstar_bench