VisToolBench (Parquet, multi‑image) — 25 single‑turn + 25 multi‑turn

Short description. This dataset package contains a 50‑item subset formatted for the Hugging Face Dataset Viewer with embedded images and clean, minimal fields. It includes:

• 25 single‑turn items (each with exactly 1 embedded image),
• 25 multi‑turn items (each with one or more embedded images).

Images are stored as an array of 🤗 datasets.Image objects in the images column, so they render directly in the viewer (no local paths).

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Dataset Summary

• Source files: two JSON inputs (single‑turn and multi‑turn) sharing a compatible schema (task_id, turncase, prompt_category, eval_focus, prompt, rubrics, etc.).
• Sampling & balance: selects 25 single + 25 multi, balanced equally across the top‑K categories of --balance_on (default: prompt_category, K=5 → 5 per category per split).
• Images: local paths from the JSON are opened and embedded into the Parquet as PIL images via the 🤗 datasets.Image feature. No local file paths are stored in the Parquet.
• Rubrics: the rubrics column is an array. Single‑turn rows contain one rubric object (JSON‑encoded string). Multi‑turn rows can contain multiple rubric entries (JSON‑encoded strings), typically collected from turns if available.
• Turn type: use turncase ("single-turn" or "multi-turn") to distinguish types. No separate split or taxonomy columns are included.

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Data Fields

Field	Type	Description
id	string	Unique identifier (task_id).
turncase	string	"single-turn" or "multi-turn".
prompt_category	string	Coarse topic/domain label.
eval_focus	string	Evaluation sub‑category / capability focus.
prompt	string	The user prompt (top‑level for single‑turn; multi‑turn prompts live in turns but this field carries the main/initial prompt when present).
images	Sequence(Image())	Array of embedded images. Single‑turn items have 1; multi‑turn may have 1+.
rubrics	Sequence(string)	Array of JSON‑encoded rubric entries. Single‑turn: exactly 1; multi‑turn: 1+.

No local file paths are stored in the Parquet. Images are fully embedded for HF viewer compatibility.

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Data Instances

Single‑turn (1 image, 1 rubric entry):

{
  "id": "7f24b...",
  "turncase": "single-turn",
  "prompt_category": "engineering",
  "eval_focus": "region_switch_qa",
  "prompt": "Count the rebar and compute the area...",
  "images": [ "<Image bytes embedded>" ],
  "rubrics": [ "{ \"criteria\": {\"count_correct\": true, ...} }" ]
}

Multi‑turn (multiple images, multiple rubric entries):

{
  "id": "a1c89...",
  "turncase": "multi-turn",
  "prompt_category": "finance",
  "eval_focus": "hybrid_tool_reasoning",
  "prompt": "Summarize the option chain from the provided screenshots...",
  "images": [ "<Image1>", "<Image2>", "<Image3>" ],
  "rubrics": [
    "{ \"turn\": 1, \"criteria\": { ... } }",
    "{ \"turn\": 3, \"criteria\": { ... } }"
  ]
}

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Example Usage

Load and preview

from datasets import load_dataset
import json

ds = load_dataset("parquet", data_files={"all": "hf_parquet_out/vistoolbench_50.parquet"})["all"]
print(ds)

# Show images for the first row
row = ds[0]
print("turncase:", row["turncase"], "num_images:", len(row["images"]))
display(row["images"][0])  # PIL.Image

# Parse rubrics (array of JSON strings)
rubrics = [json.loads(x) for x in row["rubrics"]]
print(rubrics[0])

Filtering by type or category

single = ds.filter(lambda r: r["turncase"] == "single-turn")
multi  = ds.filter(lambda r: r["turncase"] == "multi-turn")
eng    = ds.filter(lambda r: r["prompt_category"] == "engineering")