File size: 3,967 Bytes
ad844e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | ---
license: cc-by-nc-4.0
---
# 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).
---
## 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):
```jsonc
{
"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):
```jsonc
{
"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
```python
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
```python
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")
```
|