STEM Dataset

Bilingual Multimodal STEM Dataset — a curated collection of 500 Math and Physics questions in Malay and English, some enriched with relevant images.

Problem statement

AI models often struggle with bilingual and multimodal STEM tasks due to a lack of high-quality, domain-specific datasets in languages like Malay and English.

SOLUTION

We created a curated dataset of 500 Math and Physics questions in Malay and English, complemented by a public leaderboard to benchmark AI model performance.

RESULT

AI teams now have a reliable resource for fine-tuning and evaluating models on real-world STEM tasks, setting a new standard for bilingual and multimodal AI development.

Overview

A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects

‍

This dataset provides a comprehensive evaluation set for tasks assessing reasoning skills in Science, Technology, Engineering, and Mathematics (STEM) subjects. It features questions in both English and Malay, catering to a diverse audience.

‍

Key Features

Bilingual: Questions are available in English and Malay, promoting accessibility for multilingual learners.
Visually Rich: Questions are accompanied by figures to enhance understanding and support visual and contextual reasoning.
Focus on Reasoning: The dataset emphasizes questions requiring logical reasoning and problem-solving skills, as opposed to simple recall of knowledge.
Real-World Context: Questions are derived from real-world scenarios, such as past SPM (Sijil Pelajaran Malaysia) examinations, making them relatable to students.

‍

Dataset Structure

The dataset is comprised of two configurations: data_en (English) and data_ms (Malay). Both configurations share the same features and structure.

‍

Data Fields

FileName: Unique identifier for the source file (alphanumeric).
IBSN: International Standard Book Number of the source book (if available).
Subject: Academic subject (e.g., Physics, Mathematics).
Topic: Specific topic of the question within the subject (may be missing).
Questions: Main body of the question or problem statement.
Figures: List of associated image files related to the question (empty if no figures are present).
Label: Original caption or description of each image in the imgs list.
Options: Possible answer choices for the question, with keys (e.g., "A", "B", "C", "D") and corresponding text.
Answers: Correct answer to the question, represented by the key of the correct option (e.g., "C").

‍

Other Projects

Discover the work we do

View All

Structural damage classification of civil structures for a global oil and gas conglomerate

SUPA's experts scaled client's data annotation, accurately annotating 12,000+ images to boost damage classification workflow.

problem

The Client required a specialised team to identify and assess damage of civil structures with a high accuracy of at least 90%.

solution

SUPA’s engineering experts collaborated closely with the Client by co-creating the annotation workflow and assembling a team of annotators experienced with engineering-related projects.

result

SUPA’s team of 25 annotators successfully delivered the annotations with a consistent >90% accuracy.

Learn More

View On Maps

Structural damage classification of civil structures for a global oil and gas conglomerate

Advancing AI Waste Intelligence

SUPA's labeling infrastructure helped Greyparrot.ai, a global leader in AI waste intelligence, expand to 89 categories

problem

Greyparrot undertook the task of expanding its waste recognition library to encompass 89 categories, enabling a finer analysis of various waste streams.

solution

SUPA's technological infrastructure optimized the data labeling pipeline, slashing the startup time from 2-3 weeks to a mere 24 hours, all while maintaining stringent data quality standards.

result

Drawing upon SUPA's proficiency in data annotation, Greyparrot extended its waste recognition library from 49 to 89 categories; and it doesn’t stop there.

89 classes

Waste Intelligence

24-hour start-up time

Learn More

View On Maps