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README.md
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### 3. Find `.pdf` ressources.
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First check if there are already available `.pdf` in https://huggingface.co/AI-MO/olympiads-0.1
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### 4. Find `.md` ressources.
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First check if there are already available `.pdf` in https://huggingface.co/AI-MO/olympiads-0.1
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### 5. Convert `.pdf` to `.md` using Mathpix
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Use [
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Example:
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```bash
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python -m
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```
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### 6.
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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/`.
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Ask for a review.
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###
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Update the [tracker](https://docs.google.com/spreadsheets/d/1PiK-lUjcZ8VKwjtyzYWbd_bLQXnlbIPl-jmm5ebZplw/edit?gid=0#gid=0) with columns:
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###
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Create a ticket in git
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### 3. Find `.pdf` ressources.
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First check if there are already available `.pdf` in https://huggingface.co/AI-MO/olympiads-0.1
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* if yes upload them in `AI-MO/olympiads-ref/<competition>/raw/` and continue to step 4.
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* if no, find sources in internet (preferably with official solution), download and upload in `AI-MO/olympiads-ref/<competition>/raw/`
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### 4. Find `.md` ressources.
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First check if there are already available `.pdf` in https://huggingface.co/AI-MO/olympiads-0.1
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* if yes upload in `AI-MO/olympiads-ref/<competition>/md/` and continue to step 6.
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* if no, find sources in internet (preferably with official solution), download and upload in `AI-MO/olympiads-ref/<competition>/md/`
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### 5. Convert `.pdf` to `.md` using Mathpix
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Use [data_pipeline](https://github.com/project-numina/numina-math/blob/main/data_pipeline).
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Example:
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```bash
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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"
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```
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### 6. Find `.jsonl` ressources.
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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).
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* if yes, check quality and upload in `AI-MO/olympiads-ref/<competition>/segmented/` and continue to step 8.
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* if no, continue to step 7.
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### 7. Segment the `.md` files into `.jsonl`
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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/`.
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Ask for a review.
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### 8. Update the status in the trackers
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Update the [tracker](https://docs.google.com/spreadsheets/d/1PiK-lUjcZ8VKwjtyzYWbd_bLQXnlbIPl-jmm5ebZplw/edit?gid=0#gid=0) with columns:
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* status: DONE + a link to your generated data in hf
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* problem_count: count of problems in data
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* solution_count: count of solutions in data (different than problem_count since a problem can have several solutions)
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* years: range of competition years covered in your data (so we can easily track if many years are missing)
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* assignee: your name
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Update the [old tracker](https://docs.google.com/spreadsheets/d/1fw1nYQo2hN52PYTAT3SYwNTjUfjTmMRJOV84vSNxiTs) with this comumn:
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* ref: color in green for the competition you segmented
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### 9. Integrate the data in a base dataset
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Create a ticket in git
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