Dataset Card for MathVista

Dataset Description Paper Information Dataset Examples Leaderboard Dataset Usage Data Downloading Data Format Data Visualization Data Source Automatic Evaluation

License Citation

  Dataset Description

MathVista is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of three newly created datasets, IQTest, FunctionQA, and PaperQA, which address the missing visual domains and are tailored to evaluate logical… See the full description on the dataset page: https://huggingface.co/datasets/AI4Math/MathVista.

AI4Math: Mathematical QA Dataset

9,800 Questions from IQ Tests, FunctionQA & PaperQA

By Huggingface Hub [source]

About this dataset

AI4Math is an invaluable resource for those seeking to advance their research in developing tools for mathematical question-answering. With a total of 9,800 questions from IQ tests, FunctionQA tasks, and PaperQA presentations, the dataset provides a comprehensive collection of questions with valuable annotations. This includes information on the text of the question, related images as well as a decoded version of the image, choisable answers whenever relevant to aid answering accuracy measurement (precision), and predetermined answer types along with metadata which can provide additional insight into certain cases. By making use of this dataset researchers are able to target different areas within mathematical question-answering with precision relative to their respective goals -- be it IQ tests or natural language processing based function computation -- while assessing progress through recorded accurate answers (precision). AI4Math is truly transforming how mathematics can be applied for machine learning applications one step at a time

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Before you get started with this dataset, it is important to familiarize yourself with the columns: question, image, decoded_image, choices, unit, precision​ , question_type​ , answer_type​ , metadata ​and query.
• It is advisable that you read through the data dictionary provided in order to understand which columns are given in the dataset and what type of data each column contains (for example 'question' for text questions). ​
• Once you understand what information is contained within each column – it’s time to start exploring! Use a visual exploration tool such as Tableau or Dataiku DSS to explore your data before doing any in-depth analysis or machine learning processing on it. Visual explorations can provide insights on trends across different fields including demographics and purchase history etcetera which can be interesting even if they don’t result in any direct output from machine learning or statistical models used later in analysis/prediction tasks..4 You may also want to consider using a text analyzer such as Google NL API or Word2Vec API to look for relationships between words used in certain questions and answers across all datasets – this could help you get more insight into your current datasets and plan ideas for future research . 5 Lastly make sure you always keep track of versioning when performing tasks on any large dataset – having multiple versions makes it easier for everyone involved since mistakes can always be reverted before reverting by accident everything related with completed analyses/models..6 After exploring your data its time for actual machine learning processing - depending on what type of activity need they may use supervised/unsupervised algorithm approaches , neural networks etcetera trying out multiple solutions looks like a good idea since some techniques might work better than others depending specific problem at hand 7 After running several experiments track down results keeping notes nearby metrics obtained along process not only during predictions but also training 8 Finally its very important evaluate models after every cycle making sure their performance stable ; many times accuracy improvement more reliable indicator valid model rather than metrics like accuracy itself 9 If satisfied results set watch performance continuously over time checking ongoing basis if everything still works correctly 10 To keep up date new developments regarding technologies being used its highly recommended subscribing mailing lists leading software products companies whose solutions using regularly

Research Ideas

• Using the metadata and question columns to develop algorithms that automatically generate questions for certain topics as defined by the user.
• Utilizing the image column to create a computer vision model for predicting and classifying similar images.
• Analyzing the content in both the choices and answer_type columns for extracting underlying patterns in IQ Tests, FunctionQA tasks, and PaperQA presentations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy,...