README.md

---
license: cc-by-4.0
pretty_name: Wonderbread
task_categories:
  - text-generation
tags:
  - web-agent
  - bpm
  - wonderbread
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: parquet/*.parquet
    columns:
      - workflow_id
      - task_id
      - intent
      - site
      - start_url
      - sop
  - config_name: full
    data_files:
      - split: train
        path: parquet/*.parquet
size_categories:
  - 1K<n<10K
---

# Wonderbread (Text Only)

`zstd`-compressed Parquet conversion of the JSON/Text traces from the [WONDERBREAD](https://wonderbread.stanford.edu/) dataset ([Paper](https://arxiv.org/abs/2406.13264), [Original Zenodo](https://zenodo.org/records/12671568)).

The original 7.4GB of text-only traces has been packed into a single Parquet file; The highly repetitive AXTrees and HTML DOM structures in the dataset compress extremely efficiently.

Data subsets:
- `default` (viewer-safe): excludes the heavy `trace` JSON strings to prevent browser OOM on the Hugging Face dataset viewer.
- `full`: includes all columns, including the raw DOM snapshots and action history in the `trace` column.

## Dataset Structure

Each row represents a single workflow demonstration:

- `workflow_id` (string): The original trace ID including the timestamp (e.g., `102 @ 2023-12-27-00-57-01`)
- `task_id` (int32): The parsed integer WebArena task ID (e.g., `102`)
- `intent` (string): The natural language intent of the task (e.g., `What are the top-2 best-selling product in 2022`)
- `site` (string): The WebArena site (e.g., `shopping_admin`)
- `start_url` (string): The starting URL of the workflow
- `sop` (string): The raw text of the Standard Operating Procedure (`SOP - ...txt`)
- `trace` (string, **only in `full` config**): The raw stringified JSON array containing the DOM states and actions.

## Usage

```python
from datasets import load_dataset
import json

# Lightweight subset (default, viewer-safe)
ds = load_dataset("tkukurin/wonderbread")
train_split = ds["train"]

# Full dataset with the heavy JSON trace strings
ds_full = load_dataset("tkukurin/wonderbread", "full")

# Example: Parsing the JSON trace back into a list of steps
sample_trace_str = ds_full["train"][0]["trace"]
trace_list = json.loads(sample_trace_str)
```

## Citation
```bibtex
@inproceedings{wornow2024wonderbread,
  title={WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks},
  author={Wornow, Michael and others},
  booktitle={arXiv preprint arXiv:2406.13264},
  year={2024}
}
```