Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+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")
+```