File size: 2,894 Bytes
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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
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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`](https://huggingface.co/datasets/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:
```python
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`](https://huggingface.co/datasets/craigwu/vstar_bench/viewer/default/test)
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