README.md

---
pretty_name: Olympiads Reference Dataset
dataset_info:
  features:
    - name: year
      dtype: string
    - name: tier
      dtype: string
    - name: problem_label
      dtype: string
    - name: problem_type
      dtype: string
    - name: problem
      dtype: string
    - name: solution
      dtype: string
    - name: metadata
      struct:
        - name: resource_path
          dtype: string
        - name: problem_match
          dtype: string
        - name: solution_match
          dtype: string
configs:
  - config_name: default
    data_files:
      - split: train
        path: '**/segmented/**/*.jsonl'
---

# 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:

 ```json
{
  "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](https://docs.google.com/spreadsheets/d/1PiK-lUjcZ8VKwjtyzYWbd_bLQXnlbIPl-jmm5ebZplw/edit?gid=0#gid=0) and assign yourself one line by updating columns:
* status: IN PROGRESS
* assignee: your name

### 2. Setup 
Download data locally.
```bash
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 [data_pipeline](https://github.com/project-numina/numina-math/blob/main/data_pipeline). 
Example:

```bash
python -m data_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. Find `.jsonl` ressources. 
First check if there are already segmentaions available `.jsonl` in https://huggingface.co/datasets/AI-MO/olympiads-0.3. You can check if the segmentation has been done in this [old tracker](https://docs.google.com/spreadsheets/d/1fw1nYQo2hN52PYTAT3SYwNTjUfjTmMRJOV84vSNxiTs).
* if yes, check quality and upload in `AI-MO/olympiads-ref/<competition>/segmented/` and continue to step 8. 
* if no, continue to step 7. 

### 7. Segment the `.md` files into `.jsonl`

Write a `segment.py` that can be applied to your data (please do sanity checks!). Examples are [this](https://huggingface.co/datasets/AI-MO/olympiads-ref/blob/main/IMO/segment_script/segment.py) or [that](https://huggingface.co/datasets/AI-MO/olympiads-ref/blob/main/IMO/segment_script/segment_compendium.py). 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.

### 8. Update the status in the trackers

Update the [tracker](https://docs.google.com/spreadsheets/d/1PiK-lUjcZ8VKwjtyzYWbd_bLQXnlbIPl-jmm5ebZplw/edit?gid=0#gid=0) 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

Update the [old tracker](https://docs.google.com/spreadsheets/d/1fw1nYQo2hN52PYTAT3SYwNTjUfjTmMRJOV84vSNxiTs) with this comumn:
* ref: color in green for the competition you segmented

### 9. Integrate the data in a base dataset

Create a ticket in git