Datasets:

vistoolbench
/

VisToolBench

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).

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.

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.

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\": { ... } }"
  ]
}

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")