---
title: Open-Jev-v1.1
canonical_url: "https://www.modelscope.cn/datasets/ZefanCai/Open-Jev-v1.1"
md_url: "https://www.modelscope.cn/datasets/ZefanCai/Open-Jev-v1.1.md"
repository: ZefanCai/Open-Jev-v1.1
last_updated: 2026-09-24
license: other
downloads: 576
stars: 0
---

# Open-Jev-v1.1

> Open-Jev-v1.1 - ZefanCai 在 ModelScope 开源的数据集。This release publishes 326,619 typed decision records, including 147,139 training records, from the frozen community-hard-mix-v2-final mixture used for the new Open-Jev 27B training stage. It is a redistributable…

ZefanCai/Open-Jev-v1.1 是 ModelScope 魔搭社区上的数据集，采用 other 许可。

- **Repository**: ZefanCai/Open-Jev-v1.1
- **License**: other
- **Downloads**: 576
- **Stars**: 0
- **Last updated**: 2026-09-24

Source: https://www.modelscope.cn/datasets/ZefanCai/Open-Jev-v1.1

---

# Open-Jev v1.1 data

This release publishes **326,619 typed decision records**, including **147,139 training records**, from the frozen `community-hard-mix-v2-final` mixture used for the new Open-Jev 27B training stage. It is a **redistributable projection**, not the exact complete training or evaluation dataset: 2,053 Wikispeedia records are omitted because a separate redistribution license for the archived graph/path data has not been verified.

[Project website](https://zefan-cai.github.io/open-jev/) · [Code](https://github.com/Zefan-Cai/Open-Jev) · [Previous data release](https://huggingface.co/datasets/ZefanCai/Open-Jev) · [Collection](https://huggingface.co/collections/ZefanCai/open-jev)

## Exact split counts

| Split | Complete frozen mixture | Published projection | Wiki rows omitted |
|---|---:|---:|---:|
| Train | 148,639 | 147,139 | 1,500 |
| Calibration | 26,764 | 26,675 | 89 |
| Validation | 25,482 | 25,413 | 69 |
| Test | 43,301 | 43,125 | 176 |
| OOD | 84,486 | 84,267 | 219 |
| Total | 328,672 | 326,619 | 2,053 |

The complete mixture manifest SHA-256 is `7b26f948d2ae11f20d7a18be437d1ada616fc479e1596ae94be7596787fe4e54`. Retained rows preserve their original bytes, order, IDs, target distributions and split membership. No Wiki task text, options, graph or paths are included. The exact original manifest and exclusion positions with content hashes are provided for reconstruction with separately obtained Wiki records.

**Training and evaluation scope:** the 27B stage trained on the complete 148,639-row training split for 37,160 global-batch-four optimizer steps. The final step wraps one training row. Published projection counts must not replace those training counts. The 512-row internal Test/OOD panels and the complete internal splits include their original Wiki rows; published metrics are not measurements on this filtered projection. Accuracy excludes soft-target rows, while distribution metrics can use all rows. This v1.1 name identifies the data release and does not imply that every model size has completed training on it.

## What changed

The complete mixture adds 51,200 original controlled decision rows across eight authored domains: support, information retrieval, contracts, RAG, browser tools, shell history, games and rubric judging. These cover 80 authored policy families and 6,400 underlying scenarios; related views and counterfactuals are correlated. They are not 80 independently collected external sources or proof of broad real-world coverage.

It also contains **101,207 WANLI-derived English natural-language-inference records**: 82,045 Train, 4,707 Calibration, 4,830 Validation, 4,998 Test and 4,627 OOD. The remaining records replay existing Open-Jev controls. Replay training is capped at 1,500 rows per source, with hash-ordered round-robin selection across parent groups; retained new-source training rows are used once per pass. Older held-out records retain their existing split assignments.

WANLI is one English NLI source. Its OOD split holds out connected seed/premise components within that source; it is not a new-language or new-domain benchmark. Other OOD definitions depend on each corpus and often reserve templates, rule families, layouts or goals. Test/OOD composition differs from the earlier release, so aggregate scores across versions are not a paired comparison.

## Load and recover the original record

```python
import json
from datasets import load_dataset

ds = load_dataset("ZefanCai/Open-Jev-v1.1", "community-hard-mix-v2-redistributable")
record = json.loads(ds["train"][0]["record_json"])
```

Use a pinned dataset commit for reproducible experiments. The Parquet schema contains `id`, `group_id`, `split`, `source`, `kind`, `question`, `options`, `target`, `state_json`, `metadata_json`, `record_json`, and `original_line_number`. JSON string columns preserve heterogeneous state/metadata without forcing a shared nested Arrow schema. `record_json` decodes to the original training record; `original_line_number` refers to its position in the complete mixture, so gaps identify omitted rows.

`kind` is `choice`, `noul` or `score`. Targets are distributions over the supplied options; they are not automatically empirical uncertainty estimates. Read `metadata.target_basis`: labels may come from exact synthetic rules, a known stochastic construction, human annotations or uniform sets of optimal actions. For Noul, the distribution is `[P(no), P(yes)]`. Dataset metadata, control latents and target distributions are supervision/provenance fields, not model input. The model input comprises `state`, `question`, `kind` and `options`.

The deterministic gzip files under `raw/` preserve the exact retained JSONL bytes. Parquet/raw round-trip verification and original/public split hashes are recorded in `export-manifest.json`. To restore the full original mixture, supply separately obtained `wikispeedia-v1` split files:

```bash
python reproduce/restore_original_mixture.py \
  --release-root /path/to/downloaded/Open-Jev-v1.1 \
  --wiki-dir /path/to/separately-obtained/wikiracing \
  --output /path/to/new/full-original-mixture
```

The utility selects the exact retained Wiki subset by canonical record hashes, including the 1,500-row training replay subset, restores row order and verifies every original split checksum. It does not download the Wiki archive or grant rights to it.

## Licensing and attribution

This is a source-dependent release. **WANLI text and derivatives remain CC BY 4.0**. Original generated Open-Jev content is dedicated under **CC0 1.0**; source code is MIT. These terms do not relicense external wording, assets, model weights or datasets. Customer-control provenance retains its existing qualification: generated conversations are CC0, while short upstream question descriptions are from TypeSafe documentation whose source license was not verified. See [LICENSE-DATA.md](LICENSE-DATA.md) and [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).

WANLI attribution: Alisa Liu, Swabha Swayamdipta, Noah A. Smith and Yejin Choi (2022), *WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation*. [Source repository](https://huggingface.co/datasets/alisawuffles/WANLI/tree/61c95318fd71c55b6ba355d76253254615f387ec), pinned revision `61c95318fd71c55b6ba355d76253254615f387ec`; [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). The source dataset card is retained in `provenance/WANLI-README.md`.

Modifications: premise/hypothesis pairs become a typed Choice over entailment, contradiction and neutral; premise text, hypothesis text and retained human gold are preserved. Options are deterministically reordered. Connected MNLI-seed/normalized-premise components are kept within splits, official Test components are excluded from Train/Calibration/Validation/OOD, and duplicate or conflicting pairs are removed. Every WANLI record retains original IDs, source split, revision, source-file hash, attribution and conversion notes. Human-reviewed labels may still be wrong.

## Evaluation separation and limits

No JevBench or Frontier-100 task payload is published here. The recorded lexical screen compared the complete 148,639 training rows against 531 frozen evaluation requests using visible state/question/options and found no matching rows under that screen. It did not use benchmark answers or rationales. This does **not** prove absence of paraphrases, semantic overlap or foundation-model pretraining exposure. See the copied screening evidence in `provenance/benchmark-overlap-screen.json`.

Export validation checks unique row IDs, cross-split parent-group separation, source hashes, labels, omission counts and exact Parquet/raw round trips. Such checks establish data integrity, not semantic correctness or benchmark performance. No evaluation payload, API credentials, private mailbox contents, game ROMs, media assets or model weights are included in this dataset release.