Create README.md

README.md

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+---
+language:
+  - en
+task_categories:
+  - question-answering
+  - visual-question-answering
+pretty_name: VSR (Parquet)
+dataset_info:
+  features:
+    - name: question
+      dtype: string
+    - name: question_type
+      dtype: string
+    - name: answer
+      dtype: string
+    - name: image
+      sequence:
+        dtype: image
+    - name: id
+      dtype: string
+    - name: gt_value
+      dtype: bool
+    - name: relation
+      dtype: string
+    - name: subj
+      dtype: string
+    - name: obj
+      dtype: string
+    - name: image_file
+      sequence:
+        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](https://github.com/cambridgeltl/visual-spatial-reasoning) 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:
+```json
+{"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]`).
+
+