README.md

metadata

language:
  - en
task_categories:
  - question-answering
  - visual-question-answering
pretty_name: VSR (Parquet)
dataset_info:
  features:
    - name: index
      dtype: string
    - name: question
      dtype: string
    - name: question_type
      dtype: string
    - name: answer
      dtype: string
    - name: image
      sequence:
        dtype: image
    - name: image_file
      sequence:
        dtype: string
    - name: id
      dtype: string
    - name: text
      dtype: string
    - name: gt_value
      dtype: bool
    - name: relation
      dtype: string
    - name: subj
      dtype: string
    - name: obj
      dtype: string
  splits:
    - name: test
configs:
  - config_name: default
    data_files:
      - split: test
        path: VSR_Zero_Shot_Test.parquet

VSR (Parquet + TSV)

This repo provides a Parquet-converted VSR dataset and a TSV formatted for vlmevalkit.

Contents

• VSR_Zero_Shot_Test.parquet

  • Columns:
    • question (string) — adds <image> placeholders (from the original text) and appends options + post prompt (see below)
    • question_type (string)
    • answer (string; "A" for True, "B" for False)
    • image (list[image]) — image bytes aligned with the <image> order
    • id (string)
    • gt_value (bool; original True/False)
    • relation (string)
    • subj (string)
    • obj (string)
    • image_file (list[string]; original image file names)

• VSR_Zero_Shot_Test.tsv (for vlmevalkit)

  • Columns:
    • index (string; from id)
    • category (string; from question_type)
    • image (string)
      • single image → base64 string
      • multiple images → JSON array string of base64 strings
      • no image → empty string
    • question (string)
    • answer (string; "A" or "B")
    • A (string; literal "True")
    • B (string; literal "False")
    • other fields mirrored from jsonl: id, question_type, relation, subj, obj, image_file, etc.

How we build question from the original VSR

Each original record contains:

{"id": "...", "image": ["000000085637.jpg"], "text": "<image>\nThe bed is under the suitcase.", "gt_value": true, "question_type": "vsr", "relation": "under", "subj": "bed", "obj": "suitcase"}

We construct the final question as:

1. Take the original text (which already contains <image> placeholders).
2. Append the fixed options block:

Options:
A. True
B. False

3. Append the post prompt (default):

Is this statement True or False? Answer with the option's letter.

So, the final question looks like:

<image>
The bed is under the suitcase.

Options:
A. True
B. False
Is this statement True or False? Answer with the option's letter.

The answer is "A" if gt_value is true, otherwise "B".

Notes

• <image> placeholders are preserved in question and used to interleave images and text inside vlmevalkit prompts.
• Options (A. True, B. False) and the post prompt are embedded into question, so dataset consumers do not need to add choices externally.
• TSV uses base64-encoded images (string or JSON array string), while Parquet stores raw image bytes (list[image]).