Datasets:
File size: 2,589 Bytes
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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}
}
```
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