|
--- |
|
license: cc-by-4.0 |
|
dataset_info: |
|
- config_name: data-ms |
|
features: |
|
- name: file name |
|
dtype: string |
|
- name: IBSN |
|
dtype: string |
|
- name: subject |
|
dtype: string |
|
- name: topic |
|
dtype: string |
|
- name: Questions |
|
dtype: string |
|
- name: figures |
|
sequence: image |
|
- name: label |
|
sequence: string |
|
- name: Options |
|
dtype: string |
|
- name: Answers |
|
dtype: string |
|
splits: |
|
- name: eval |
|
num_bytes: 34663548 |
|
num_examples: 614 |
|
download_size: 34559856 |
|
dataset_size: 34663548 |
|
- config_name: data_en |
|
features: |
|
- name: FileName |
|
dtype: string |
|
- name: IBSN |
|
dtype: string |
|
- name: Subject |
|
dtype: string |
|
- name: Topic |
|
dtype: string |
|
- name: Questions |
|
dtype: string |
|
- name: Figures |
|
sequence: image |
|
- name: Label |
|
sequence: string |
|
- name: Options |
|
dtype: string |
|
- name: Answers |
|
dtype: string |
|
splits: |
|
- name: eval |
|
num_bytes: 34663548 |
|
num_examples: 614 |
|
download_size: 69119656 |
|
|
|
dataset_size: 69327096.0 |
|
tags: |
|
- mathematics |
|
- physics |
|
- llms |
|
- Malaysia |
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- Asia |
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size_categories: |
|
- n<1K |
|
configs: |
|
- config_name: data_en |
|
data_files: |
|
- split: eval |
|
path: data_en/train-* |
|
- config_name: data_ms |
|
data_files: |
|
- split: eval |
|
path: data_ms/train-* |
|
language: |
|
- en |
|
- ms |
|
--- |
|
|
|
# **A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects** |
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|
|
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** |
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|
|
* **Bilingual:** Questions are available in English and Malay, promoting accessibility for multilingual learners. |
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* **Visually Rich:** Questions are accompanied by figures to enhance understanding and support visual and contextual reasoning. |
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* **Focus on Reasoning:** The dataset emphasizes questions requiring logical reasoning and problem-solving skills, as opposed to simple recall of knowledge. |
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* **Real-World Context:** Questions are derived from real-world scenarios, such as past SPM (Sijil Pelajaran Malaysia) examinations, making them relatable to students. |
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|
|
**Dataset Structure** |
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|
|
The dataset is comprised of two configurations: `data_en` (English) and `data_ms` (Malay). Both configurations share the same features and structure. |
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**Data Fields** |
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* **FileName:** Unique identifier for the source file (alphanumeric). |
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* **IBSN:** International Standard Book Number of the source book (if available). |
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* **Subject:** Academic subject (e.g., Physics, Mathematics). |
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* **Topic:** Specific topic of the question within the subject (may be missing). |
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* **Questions:** Main body of the question or problem statement. |
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* **Figures:** List of associated image files related to the question (empty if no figures are present). |
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* **Label:** Original caption or description of each image in the `imgs` list. |
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* **Options:** Possible answer choices for the question, with keys (e.g., "A", "B", "C", "D") and corresponding text. |
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* **Answers:** Correct answer to the question, represented by the key of the correct option (e.g., "C"). |
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|
|
--- |
|
|
|
## Data Instance Example |
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|
|
```json |
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{ |
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"FileName": "FC064244", |
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"ISBN": "9786294703681", |
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"Subject": "Physics", |
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"Topic": "Measurement", |
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"Questions": "State the physical quantity that can be measured using the measuring device shown in Diagram 1.", |
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"Figures": [ |
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{ |
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"label": "Diagram 1", |
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"path": "FC064244_C1_Q12_ImageFile_0.png" |
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} |
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], |
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"Options": { |
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"A": "Weight", |
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"B": "Mass", |
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"C": "Amount of substance", |
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"D": "Volume" |
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}, |
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"Answers": "B" |
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} |
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``` |
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**Data Split** |
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|
|
The dataset is split between Physics and Mathematics subjects, with some questions lacking topic categorization. |
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| Subject | Instances with Topic | Instances without Topic | Total | |
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|-------------|----------------------|-------------------------|-------| |
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| Physics | 316 | 77 | 393 | |
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| Mathematics | 32 | 189 | 221 | |
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**Known Limitations** |
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|
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* **Subject Coverage:** The current version focuses on Physics and Mathematics. Future releases will include more STEM subjects. |
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* **Answer Accuracy:** Answers are extracted from various sources and may contain inaccuracies. |
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**Source** |
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|
The dataset is derived from a combination of resources, including: |
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* SPM past-year exams |
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* SPM mock exams |
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* Educational exercise books |
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|
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**Data Acquisition Method** |
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|
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* Optical Character Recognition (OCR) for text extraction |
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* Manual quality control (QC) to ensure data accuracy |
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|
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**Versioning and Maintenance** |
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* **Current Version:** 1.0.0 |
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* **Release Date:** December 27, 2024 |
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* **Contact:** We welcome any feedback or corrections to improve the dataset quality. |
|
|
|
--- |
|
|
|
# License |
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|
|
This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). |
|
|
|
--- |
|
|
|
# Getting Started |
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|
|
You can access the dataset on Hugging Face using the following commands: |
|
|
|
```bash |
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# For English data |
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pip install datasets |
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|
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from datasets import load_dataset |
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dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_en") |
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|
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# For Malay data |
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dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_ms") |
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``` |
|
--- |
|
|
|
# Bilingual STEM Dataset LLM Leaderboard |
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|
|
This document summarizes the evaluation results for various language models based on **5-shot** and **First Token Accuracy**. The evaluation was conducted across four configurations: |
|
|
|
| **Model** | **en\_withfigures** | **en\_withoutfigures** | **ms\_withfigures** | **ms\_withoutfigures** | |
|
| --------------------------------- | ------------------- | ---------------------- | ------------------- | ---------------------- | |
|
| **gemini-2.0-flash-exp** | **63.70%** | <ins>75.16%</ins> | **63.36%** | <ins>75.47%</ins> | |
|
| **gemini-1.5-flash** | 49.66% | 67.39% | 50.00% | 64.28% | |
|
| **Qwen/Qwen2-VL-72B-Instruct** | <ins>58.22%</ins> | 69.25% | <ins>57.53%</ins> | 63.66% | |
|
| **gpt-4o** | 47.95% | 66.15% | 50.00% | 68.01% | |
|
| **gpt-4o-mini** | 41.10% | 55.90% | 38.36% | 52.80% | |
|
| **pixtral-large-2411** | 42.81% | 64.29% | 35.27% | 60.87% | |
|
| **pixtral-12b-2409** | 24.66% | 48.45% | 24.66% | 39.13% | |
|
| **DeepSeek-V3** | None | **79.19%** | None | **76.40%** | |
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| **Qwen2.5-72B-Instruct** | None | 74.53% | None | 72.98% | |
|
| **Meta-Llama-3.3-70B-Instruct** | None | 67.08% | None | 58.07% | |
|
| **Llama-3.2-90B-Vision-Instruct** | None | 65.22% | None | 58.07% | |
|
| **sail/Sailor2-20B-Chat** | None | 66.46% | None | 61.68% | |
|
| **mallam-small** | None | 61.49% | None | 55.28% | |
|
| **mistral-large-latest** | None | 60.56% | None | 53.42% | |
|
| **google/gemma-2-27b-it** | None | 58.07% | None | 57.76% | |
|
| **SeaLLMs-v3-7B-Chat** | None | 50.93% | None | 45.96% | |
|
|
|
--- |
|
|
|
## Notes |
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|
|
In the repository, there is an `eval.py` script that can be used to run the evaluation for any other LLM. |
|
|
|
The evaluation results are based on the specific dataset and methodology employed. |
|
- The "First Token Accuracy" metric emphasizes the accuracy of predicting the initial token correctly. |
|
- Further analysis might be needed to determine the models' suitability for specific tasks. |
|
|
|
### Attribution for Evaluation Code |
|
The `eval.py` script is based on work from the MMLU-Pro repository: |
|
- Repository: [TIGER-AI-Lab/MMLU-Pro](https://github.com/TIGER-AI-Lab/MMLU-Pro) |
|
- License: Apache License 2.0 (included in the `NOTICE` file) |
|
|
|
--- |
|
|
|
# **Contributors** |
|
- [**Gele**](https://huggingface.co/Geleliong) |
|
- [**Ken Boon**](https://huggingface.co/caibcai) |
|
- [**Wei Wen**](https://huggingface.co/WeiWen21) |
|
|
|
|
