Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 3,412 Bytes
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license: cc-by-4.0
language:
- en
task_categories:
- text-classification
tags:
- web-agent
- browser-agent
- web-navigation
- element-grounding
- mind2web
pretty_name: WebChain Elements
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: test
path: data/test.jsonl
---
# WebChain Elements — human browser trajectories as element-level decision steps
**10,930 steps** (2,555 tasks, 243 real websites) from [WebChain](https://huggingface.co/datasets/webagentlab/webchain)
(webagentlab, CC-BY-4.0) turned into one-step decision examples in the text format browser-agent decision heads use:
the task goal, the page (URL, title, visible text), the list of interactive elements, the actions taken so far, and the
**gold operation + gold element** the human chose.
| split | steps | tasks | websites | CLICK / TYPE_TEXT / PRESS_ENTER |
|---|---|---|---|---|
| train | 8,435 | 1,935 | 199 | 7,273 / 1,053 / 109 |
| test | 2,495 | 620 | 44 | 2,109 / 364 / 22 |
The test split holds out whole **websites** (none of its 44 sites appears in train). Tasks are mostly multi-constraint
(filters, sorts, dates) on shopping, travel, real-estate and job sites.
## Fields
```
goal the task, e.g. "Look for the lowest-priced women's double-breasted trench coats."
website the site's primary host; domain / intent: WebChain's labels
url, title the page the step was taken on; page_text: its visible text (<= 6,000 chars)
candidates interactive elements (≤ 60, page order): {id, kind: click|fill, label, role, value?, checked?, ...}
history earlier steps of the same trajectory: {action, kind, text, page_changed}
gold_op CLICK | TYPE_TEXT | PRESS_ENTER
gold_id the chosen candidate's id (click:N / fill:N; "press_enter" for PRESS_ENTER)
gold_label its label; gold_text: the typed text for TYPE_TEXT
```
## How it was built
WebChain records, per step, a CSS selector plus links to the page's accessibility tree (AX) and DOM snapshot. For 3,000
randomly chosen trajectories (seed 0), every step's AX tree was fetched and flattened; the **gold element was recovered
exactly** by resolving the step's CSS selector in the DOM snapshot and following its `data-imean-axt-id` to the AX node
(text matching only as a fallback — it disagreed with the selector on ~13 % of a sample, so it is never the default).
Candidates are the page's interactive AX nodes plus some non-semantic text nodes (the clicked target is often a
`<span>`/`<div>`), capped at 60 in page order. Steps whose gold element has **no label** or whose label is **duplicated**
among the candidates were dropped (they cannot be learned from text), as were hover / drag / copy / paste steps.
WebChain has no scroll and no explicit "task done" steps, so neither appears here.
Converter: [`code/finetune/convert_webchain.py`](https://huggingface.co/cklxx/laya-browser/blob/main/code/finetune/convert_webchain.py)
in [cklxx/laya-browser](https://huggingface.co/cklxx/laya-browser), whose model v19s was trained on the train split
(held-out test: click operation 0.94, click target top-1 0.48).
## License and attribution
CC-BY-4.0, derived from WebChain by webagentlab
([paper](https://arxiv.org/abs/2603.05295), [dataset](https://huggingface.co/datasets/webagentlab/webchain)).
Page texts come from public websites as recorded in WebChain.
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