README.md

metadata

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

MoJev-Mix

Contact: contact@molemo.org

The training and evaluation mixture for 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
Model	MoLeMo-Lab/mojev
Dataset	MoLeMo-Lab/mojev-mix
Results	MoJev results
Preprint	MoJev (PDF)
Project page	MoJev

Load

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

{
  "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: `<

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

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 by Zefan-Cai et al. under the MIT license. The grading and balancing code is also MIT licensed.