task_categories:image-to-text task_categories:text-to-image language:en license:mit size_categories:100K<n<1M format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
task_categories:
- image-to-text
- text-to-image
language:
- en
pretty_name: simons ARC (abstraction & reasoning corpus) solve mass version 7
size_categories:
- 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data.jsonl
---

# Version 1

ARC-AGI Tasks where the job is to identify the mass of objects with a specific size.

example count: 2-4.

test count: 1-2.

image size: 4-6.

find mass: 1-2.

connectivity: ALL8.

# Version 2

image size: 3-10.

# Version 3

image size: 1-15.

# Version 4

image size: 1-15.

find mass: 1-3.

# Version 5

image size: 1-8.

find mass: 1-4.

# Version 6

Compare mass of adjacent rows/columns. image size: 4-7. color count: 10.

This was something that the model struggles with. What if I use fewer colors and smaller images.

# Version 7

Compare mass of adjacent rows/columns. image size: 4-6. color count: 2,3,10.
