pretty_name: Olympiads Reference Dataset
AI-MO Olympiad Reference Dataset
This dataset contains a structured collection of Olympiad problems and their solutions, organized by competition. Contains high quality data, prioritizing "official" solutions to problems.
Structure
<competition name>/ # Problems and solutions from the International Mathematical Olympiad
├── raw/ # Raw problem/solution statements (.pdf)
│ ├── file1.pdf
│ ├── file2.pdf
├── download_script/ # the scripts used to download raw data
│ ├── download.py
├── md/ # .md files generated from raw/ files
│ ├── file1.md
│ ├── file2.md
├── segment_script/ # the scripts used to segment the data
│ ├── segment.py
└── segmented/ # .jsonl segmented data for easier processing
├── file1.jsonl
├── file2.jsonl
└── file3.jsonl
Each json in jsonl file follows this structure:
{
"problem": "string", // Mandatory: The problem statement in latex or markdown
"solution": "string", // Mandatory: The solution for the problem
"year": "int", // Optional: Year when the problem was presented
"problem_type": "string", // Optional: The mathematical domain of the problem. Here are the supported types:
//['Algebra', 'Geometry', 'Number Theory', 'Combinatorics', 'Calculus',
//'Inequalities', 'Logic and Puzzles', 'Other']
"question_type": "string", // Optional: The form or style of the mathematical problem.
// The supported classes are: ['MCQ', 'proof' or 'math-word-problem'].
// 'math-word-problem' is a problem with output.
"answer": "string", // Optional: final answer is the question_type is "math-word-problem".
"source": "string", // Optional: TODO:describe
"exam": "string", // Optional: TODO:describe
"difficulty": "int", // Optional: TODO:describe
"other": "...", // Optional: You can add other fields with metadata
}
Steps to collect data for formalization
1. Assign yourself a task
Check the tracker and assign yourself one line by updating columns:
- status: IN PROGRESS
- assignee: your name
2. Setup
Download data locally.
git lfs install
git clone git@hf.co:datasets/AI-MO/olympiads-ref
3. Find .pdf ressources.
First check if there are already available .pdf in https://huggingface.co/AI-MO/olympiads-0.1
* if yes upload them in AI-MO/olympiads-ref/<competition>/raw/ and continue to step 4.
* if no, find sources in internet (preferably with official solution), download and upload in AI-MO/olympiads-ref/<competition>/raw/
4. Find .md ressources.
First check if there are already available .pdf in https://huggingface.co/AI-MO/olympiads-0.1
* if yes upload in AI-MO/olympiads-ref/<competition>/md/ and continue to step 6.
* if no, find sources in internet (preferably with official solution), download and upload in AI-MO/olympiads-ref/<competition>/md/
5. Convert .pdf to .md using Mathpix
Use new_pipeline. Example:
python -m new_pipeline convert_to_md --method=pdf_to_md --input_dir="/home/marvin/workspace/olympiads-ref/IMO/raw" --output_dir="/home/marvin/workspace/olympiads-ref/IMO/md"
6. Segment the .md files into .jsonl
Write a segment.py that can be applied to your data (please do sanity checks!). Examples are this or that. Once you are fine with your segmentation upload the .jsonl in AI-MO/olympiads-ref/<competition>/segmented/ and the segment.py in AI-MO/olympiads-ref/<competition>/segment_script/.
Ask for a review.
7. Update the status in the tracker
Update the tracker with columns: * status: DONE + a link to your generated data in hf * problem_count: count of problems in data * solution_count: count of solutions in data (different than problem_count since a problem can have several solutions) * years: range of competition years covered in your data (so we can easily track if many years are missing) * assignee: your name
8. Integrate the data in a base dataset
Create a ticket in git