metadata

annotations_creators:
  - machine-generated
language:
  - en
language_creators:
  - machine-generated
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: CLEVR-Math - Compositional language, visual, and mathematical reasoning
size_categories: null
source_datasets:
  - clevr
tags:
  - reasoning
  - neuro-symbolic
  - multimodal
task_categories:
  - visual-question-answering
task_ids:
  - visual-question-answering

Dataset Card for CLEVR-Math

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
- Other Known Limitations
Additional Information

Dataset Description

Homepage:
**Repository:https://github.com/dali-does/clevr-math
**Paper:https://arxiv.org/abs/2208.05358
Leaderboard:
**Point of Contact:dali@cs.umu.se

Dataset Summary

Dataset for compositional multimodal mathematical reasoning based on CLEVR.

Loading the data, preprocessing text with CLIP

from transformers import CLIPPreprocessor
from datasets import load_dataset, DownloadConfig


dl_config = DownloadConfig(resume_download=True,
        num_proc=8,
        force_download=True)

# Load 'general' instance of dataset
dataset = load_dataset('dali-does/clevr-math', download_config=dl_config)

# Load version with only multihop in test data
dataset_multihop = load_dataset('dali-does/clevr-math', 'multihop',
                                download_config=dl_config)


model_path = "openai/clip-vit-base-patch32"
extractor = CLIPProcessor.from_pretrained(model_path)
def transform_tokenize(e):
  e['image'] = [image.convert('RGB') for image in e['image']]
  return extractor(text=e['question'],
                           images=e['image'],
                           padding=True)

dataset = dataset.map(transform_tokenize,
                      batched=True,
                      num_proc=8,
                      padding='max_length')

dataset_subtraction = dataset.filter(lambda e:
        e['template'].startswith('subtraction'), num_proc=4)

Supported Tasks and Leaderboards

Leaderboard will be announced at a later date.

Languages

The dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.

Dataset Structure

Data Instances

general containing the default version with multihop questions in train and test
multihop containing multihop questions only in test data to test generalisation of reasoning

Data Fields

features = datasets.Features(
  {
    "template": datasets.Value("string"),
    "id": datasets.Value("string"),
    "question": datasets.Value("string"),
    "image": datasets.Image(),
    "label": datasets.Value("int64")
  }
)

Data Splits

train/val/test

Dataset Creation

Data is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.

Considerations for Using the Data

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Adam Dahlgren Lindström - dali@cs.umu.se

Licensing Information

Licensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).

Citation Information