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update readme

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@@ -67,37 +67,45 @@ git clone git@hf.co:datasets/AI-MO/olympiads-ref
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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 [new_pipeline](https://github.com/project-numina/numina-math/tree/yufan/new_pipeline).
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  Example:
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  ```bash
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- 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"
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  ```
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- ### 6. 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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- ### 7. Update the status in the tracker
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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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- ### 8. Integrate the data in a base dataset
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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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+
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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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+
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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