| --- |
| license: mit |
| pretty_name: MoJev-Mix |
| task_categories: |
| - multiple-choice |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - calibration |
| - preference-learning |
| - plackett-luce |
| - structured-output |
| - mojev |
| --- |
| |
| <img align="right" src="https://molemo-lab.github.io/mojev/assets/molemo-logo.png" width="76" alt="MoLeMo Lab logo"> |
|
|
| # MoJev-Mix |
|
|
| [](https://molemo-lab.github.io/mojev/) |
| [](https://github.com/MoLeMo-Lab/mojev/blob/master/paper/mojev-preprint.pdf) |
| [](https://github.com/MoLeMo-Lab/mojev) |
| [](https://huggingface.co/MoLeMo-Lab/mojev) |
|
|
| Contact: [contact@molemo.org](mailto:contact@molemo.org) |
|
|
| The training and evaluation mixture for |
| [`MoLeMo-Lab/mojev`](https://huggingface.co/MoLeMo-Lab/mojev). |
|
|
| Each row contains state, a runtime candidate set, the correct value, and—when |
| available from the source domain—a graded preference over the candidates. |
|
|
| | MoJev family resource | location | |
| |---|---| |
| | Code | [MoLeMo-Lab/mojev](https://github.com/MoLeMo-Lab/mojev) | |
| | Model | [MoLeMo-Lab/mojev](https://huggingface.co/MoLeMo-Lab/mojev) | |
| | Dataset | **MoLeMo-Lab/mojev-mix** | |
| | Results | [MoJev results](https://github.com/MoLeMo-Lab/mojev#results) | |
| | Preprint | [MoJev (PDF)](https://github.com/MoLeMo-Lab/mojev/blob/master/paper/mojev-preprint.pdf) | |
| | Project page | [MoJev](https://molemo-lab.github.io/mojev/) | |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("MoLeMo-Lab/mojev-mix") |
| print(dataset["train"][0]) |
| ``` |
|
|
| The repository also exposes the four JSONL files directly for streaming and |
| command-line workflows. |
|
|
| ## Row schema |
|
|
| ```json |
| { |
| "context": "Choose a direction to keep the snake alive ... State: {...}", |
| "options": {"answer": ["right", "down", "left"]}, |
| "preference": {"answer": [3, 0, 0]}, |
| "labels": {"answer": "right"}, |
| "source": "snake-v1", |
| "grading": "flat" |
| } |
| ``` |
|
|
| | field | type | meaning | |
| |---|---|---| |
| | `context` | string | source state rendered as text | |
| | `options` | map of string arrays | candidate values for the row | |
| | `preference` | map of integer arrays | grades aligned with `options`; higher is better | |
| | `labels` | map of strings | correct value by name | |
| | `source` | string | source generator | |
| | `grading` | string | rule used to construct the grades | |
|
|
| `schema.json` records the field definitions and maximum candidate cardinality. |
|
|
| ## Modalities |
|
|
| | modality | representation in this release | |
| |---|---| |
| | text | 507,974 rows with state serialised in `context` | |
| | image | state marker: `<|vision_start|><|image_pad|><|vision_end|>/absolute/path` | |
|
|
| The published mixture provides the text-state corpus. The same row schema accepts |
| multimodal decision rows: image assets are referenced from `context`, while |
| `options`, `preference`, and `labels` retain the same structure. Text and image |
| decisions therefore share one training and evaluation interface. |
|
|
| ## Splits |
|
|
| | split | rows | role | |
| |---|---:|---| |
| | train | 205,084 | capped training mixture | |
| | validation | 82,423 | model selection and calibration | |
| | test | 80,703 | evaluation rows from training sources | |
| | `ood` | 139,764 | evaluation rows from held-out generators | |
|
|
| Mean candidate cardinality is 4.7. Exactly 16 candidates appear in 9.8% of |
| rows; 0.4% contain more than 16. A total of 38.8% of training rows carry three |
| or more preference levels. |
|
|
| ## Preference signal |
|
|
| | grading | construction | |
| |---|---| |
| | `distance` | graph distance to the target | |
| | `palette` | RGB distance to the correct colour | |
| | `ordinal` | distance on an ordered scale | |
| | `priority` | source rule precedence | |
| | `binary`, `flat` | correct versus incorrect | |
|
|
| The builder converts 1,271,770 Open-Jev rows and caps each source within each |
| split. Capping changes painting geometry from 67.2% to 39.0% of the training |
| mixture and raises the share of rows with three or more grades from 28.5% to |
| 38.8%. |
|
|
| ## Rebuild |
|
|
| ```sh |
| git clone https://github.com/MoLeMo-Lab/mojev |
| cd mojev |
| pip install -e . |
| |
| mojev dataset-prepare openjev /path/to/Open-Jev/data/*/ \ |
| --output data/oj --max-options 64 |
| mojev dataset-prepare balance --input data/oj --output data/mix \ |
| --graded-cap 60000 --plain-cap 20000 |
| ``` |
|
|
| Both steps are deterministic: |
|
|
| - `mojev/openjev.py` converts source rows and constructs preference grades. |
| - `mojev/balance.py` applies source-level caps and writes the final splits. |
|
|
| ## Source coverage |
|
|
| The mixture is derived from 18 synthetic Open-Jev generators spanning games, |
| navigation, document states, agent traces, and palette matching. Every row uses |
| the `answer` field. Palette matching supplies most graded rows. The `source`, |
| `grading`, and candidate cardinality fields support sliced evaluation. |
|
|
| The `test` split measures new rows from the training source families. The `ood` |
| split measures new source generators. |
|
|
| ## Attribution |
|
|
| Derived from [Open-Jev](https://github.com/Zefan-Cai/Open-Jev) by Zefan-Cai et |
| al. under the MIT license. The grading and balancing code is also MIT licensed. |
|
|
