Blame for README.md

---
license: other
license_name: mixed-see-card
language:
- en
pretty_name: JEVWeb
tags:
- jev
- jev-alternative
- open-source-jev
- browser-agent
- web-navigation
- decision-model
- system-one
- distillation
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: mind2web
  data_files:
  - {split: train, path: mind2web/train.parquet}
  - {split: validation, path: mind2web/validation.parquet}
  - {split: test, path: mind2web/test.parquet}
- config_name: nnetnav_audited
  data_files:
  - {split: train, path: nnetnav_audited/train.parquet}
  - {split: validation, path: nnetnav_audited/validation.parquet}
  - {split: test, path: nnetnav_audited/test.parquet}
- config_name: webchain
  data_files:
  - {split: train, path: webchain/train.parquet}
  - {split: validation, path: webchain/validation.parquet}
  - {split: test, path: webchain/test.parquet}
- config_name: teacher_qwen3max
  data_files:
  - {split: train, path: teacher_qwen3max/train.parquet}
  - {split: validation, path: teacher_qwen3max/validation.parquet}
  - {split: test, path: teacher_qwen3max/test.parquet}
- config_name: teacher_glm
  data_files:
  - {split: train, path: teacher_glm/train.parquet}
  - {split: validation, path: teacher_glm/validation.parquet}
  - {split: test, path: teacher_glm/test.parquet}
- config_name: onpolicy_glm
  data_files:
  - {split: train, path: onpolicy_glm/train.parquet}
  - {split: validation, path: onpolicy_glm/validation.parquet}
  - {split: test, path: onpolicy_glm/test.parquet}
- config_name: bench
  data_files:
  - {split: test, path: bench/test.parquet}
- config_name: live_tasks
  data_files:
  - {split: train, path: live_tasks/train.parquet}
  - {split: test, path: live_tasks/test.parquet}
---

# JEVWeb

JEV-format views of [OpenJev-Data](https://huggingface.co/datasets/alanhuangya/OpenJev-Data): the same browser
steps, one folder per config, each step unwrapped into the three parts a decision model actually consumes.

Where OpenJev-Data stores a step as the full `POST /v1/systemone` request in a single `request` JSON string plus
flat metadata columns, every row here is `state` / `questions` / `gold`. The JSONL files keep the native nesting;
the parquet files carry the same objects as JSON strings, because a typed struct would inject null keys the source
omits (elements without `value`/`options`) into training prompts. Nothing else is added or dropped: every request in
the source splits back into these columns unchanged.

## Load it

```python
from datasets import load_dataset

m2w      = load_dataset("xiq/jevweb", "mind2web")          # human steps, split by website
nnetnav  = load_dataset("xiq/jevweb", "nnetnav_audited")   # live-web steps with audited DONE labels
webchain = load_dataset("xiq/jevweb", "webchain")          # human steps on 200+ live sites
qwen     = load_dataset("xiq/jevweb", "teacher_qwen3max")  # qwen3-max acting as System One
glm      = load_dataset("xiq/jevweb", "teacher_glm")       # GLM-5.3-Flash acting as System One
onpolicy = load_dataset("xiq/jevweb", "onpolicy_glm")      # student-visited states, GLM labels
bench    = load_dataset("xiq/jevweb", "bench", split="test")           # OpenJev-Bench, frozen
tasks    = load_dataset("xiq/jevweb", "live_tasks", split="test")      # 153 end-to-end eval tasks
```

## Configs

| Config | Rows (train / validation / test) | Source |
|---|---|---|
| `mind2web` | 5,861 / 586 / 875 | [Mind2Web](https://huggingface.co/datasets/osunlp/Mind2Web) |
| `nnetnav_audited` | 11,408 / 1,140 / 1,150 | [NNetNav-live](https://huggingface.co/datasets/stanfordnlp/nnetnav-live), stopping labels audited |
| `webchain` | 17,876 / 1,069 / 1,589 | [WebChain](https://huggingface.co/datasets/webagentlab/webchain) v2 |
| `teacher_qwen3max` | 4,781 / 385 / 57 | qwen3-max as the System One of jev-ultrafast on live sites |
| `teacher_glm` | 5,969 / 370 / 147 | GLM-5.3-Flash in the same role |
| `onpolicy_glm` | 5,782 / 275 / 119 | DAgger-style: student or teacher acts, GLM teacher labels |
| `bench` | 2,346 test | OpenJev-Bench: frozen test steps from the configs above |
| `live_tasks` | 1,402 / – / 153 | goals + start URLs, shipped unchanged (see below) |

Each split is also available as JSONL next to its parquet (`webchain/train.jsonl`, same rows, same order).

## Columns

Step configs (all but `live_tasks`):

| Column | Meaning |
|---|---|
| `state` | JSON string: `page` (url, title, visible text), `elements` (the 8–40 candidate elements offered), `recent_actions` |
| `questions` | JSON string: the typed questions for this step — `operation`, plus a target question (`click_target`, `type_text_target` or `select_target`) for each operation that needs one, each with its instructions and criteria |
| `gold` | JSON string: the gold option key per labelled question, e.g. `{"operation": "CLICK", "click_target": "7"}` |

`live_tasks` keeps the source columns `id`, `url`, `goal`, `source`: a goal and a start URL, no page state. These
rows cannot be expressed as `state` / `questions` / `gold` — the request only exists once an agent has visited the
site and the page has been parsed into elements. The 153 `test` tasks are the held-out end-to-end suite.

A step is answered correctly when the operation and, if the operation takes one, its target both match. Targets are
chosen among the candidates listed in the state, not among every element on the page. To replay a step against a
System One endpoint, rebuild the request from the two objects:
`{"state": json.loads(row["state"]), "questions": json.loads(row["questions"])}`.

## Terms

Row provenance, licences, the redaction pass and the auditing of the stopping labels are described in the
[OpenJev-Data card](https://huggingface.co/datasets/alanhuangya/OpenJev-Data), which this repo re-expresses rather
than replaces. Mind2Web-derived rows are CC BY 4.0, NNetNav-derived rows Apache-2.0, WebChain-derived rows
CC BY 4.0; teacher, on-policy and live-task rows contain text captured from public websites and remain subject to
those sites' terms and to the labelling models' provider terms. Treat them as research data and check the
applicable terms before any commercial use. Please cite OpenJev-Data and the original datasets, as that card does.