{
  "fetched_at": "2026-10-19T09:42:32Z",
  "source_url": "https://www.kaggle.com/competitions/cord-paper-clf/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The goal of this competition is to identify the categories of an arxiv paper. Papers can have one or more categories associated to it, if so then you need to predict the list of categories that belongs to that paper. Hence the problem statement is a multi-label classification problem. There is over over 26k training samples. \n\n## Files\n\n*   **train_w_targets_w_ids_v6.csv** - the training set\n*   **test_wo_targets_w_ids_v6.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format [NOT PROVIDED]\n\n\n## Columns\n\n*   `titles` - title of the research paper\n*   `summaries` - abstract of the paper\n*   `terms` - categories of the paper",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "# Goal of the Competition\nThe goal of this competition is to identify the categories associated to an research paper hosted on ArXiv. Originally a research paper can have one or more categories associated to it, if so then you need to predict the list of categories that belongs to that paper. Hence the problem statement is a multi-label classification problem.\n\n# Context\nArxiv is an open-access platform for sharing curated research articles. Over 15k papers get submitted per month on Arxiv, and it hosts more than two million scholarly articles in eight subject areas. Generally the submitter has to make sure that they mention the categories that their paper covers when they upload it. However, without having access to that meta data how can we know which category the paper belongs to only based on its title and abstract? With this competition, we attempt to answer that question using machine learning.\n\n# Acknowledgement\nThis Competition is only possible because of the CORD.ai community and its great members encouraging democratisation of AI research by means of collaboration. The competition is a part of the 1st edition of the Deep Learning Symposium organised by CORD, and we hope we will organise more great sessions and events like this in near future. If you resonate with the objective of CORD and want to be a part of this amazing community please fill up this [form](https://docs.google.com/forms/d/e/1FAIpQLScm1WHIbK6LIsYBuGkBklXlINAFWURoKBXYsLqVDHv9O4JA3w/viewform) and follow our socials,\n\n- [Website](https://cordai.org/)\n- [Twitter](https://twitter.com/CRforAi)\n- [Linkedin](https://www.linkedin.com/company/cord-ai1/mycompany/)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "# Evaluation Metric:\nThe leaderboard will be evaluated using Macro-F1 score.\n\n# Submission\nIn this competition you will submit the list categories associated with a paper. Your submission should look like the following, where `terms` is the predicted category of the paper:\n```\nids,terms\n6a7aec1bc3,['cs.CV']\nbb566a2686,['cs.LG', 'stat.ML']\ne6625c0f13,['stat.ML', 'cs.LG']\n5bf9ffc98e,['stat.ML']\n9d90e2d988,['cs.CV']\n2ae23d98cc,['cs.LG', 'cs.AI', 'cs.RO']\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n-  Using external data is not allowed.\n- Using pre-trained model is not allowed.\n- Don't cheat!\n- Apply yourself!\n- Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}