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  "fetched_at": "2026-10-24T06:26:25Z",
  "source_url": "https://www.kaggle.com/competitions/extreme-classification-eurlex/data",
  "pages": [
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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      "name": "data-description",
      "content": "<p>The dataset folder contains 3 files -</p>\n<ul>\n<li><strong>trn_ft_mat.txt</strong> - the training feature file</li>\n<li><strong>trn_lbl_mat.txt</strong> - the training label file</li>\n<li><strong>tst_ft_mat.txt</strong> - the test feature file</li>\n</ul>\n<p> <strong>Feature file format -</strong> First line specifies number of datapoints and the total number of features. Each of the following lines contains features active in that datapoint.\n<pre>&lt;#datapoints&gt; &lt;#features&gt;<br />&lt;feature_id:feature_value&gt; &lt;feature_id:feature_value&gt; &lt;feature_id:feature_value&gt; <br />&lt;feature_id:feature_value&gt; &lt;feature_id:feature_value&gt; &lt;feature_id:feature_value&gt; <br /><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>\n\n<p> <strong>Label file format -</strong> First line specifies number of datapoints and the total number of labels. Each of the following lines contains labes active in that datapoint.\n<pre>&lt;#datapoints&gt; &lt;#labels&gt;<br />&lt;label_id:1&gt; &lt;label_id:1&gt; &lt;label_id:1&gt;  <br />&lt;label_id:1&gt; &lt;label_id:1&gt;  <br /><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>\n\n<p> <strong>Reading/Writing data in Matlab -</strong> You can download the scripts from <a href=\"https://drive.google.com/open?id=17XO9kMlVLnoOJ_Ry1BYJWrUnGtTesagG\"> here</a> to read feature and label sparse matrices from .txt files into matlab: (Note that these scripts are present in the folder named matlab)</p>\n<pre>To read a text matrix into Matlab: \n       [matrix] = read_text_mat([text matrix name]);\nTo write a Matlab matrix into text format:\n       write_text_mat([Matlab sparse matrix], [text matrix name to be written to]);\n</pre>",
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      "name": "Description",
      "content": "<p>The objective in extreme multi-label learning is to learn a classifier that can automatically tag a datapoint with the most relevant subset of labels from an extremely large label set. This competition  provides a relatively small dataset called EURLex-4K which has just ~4000 labels and ~15,000 training points.</p>",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a> </p>\n<h2>Submission Format</h2>\n<p>You are required to predict <strong>top-5<\\strong> labels for each test datapoint. The file should contain a header and have the following format:</p>\n<pre>test_id,labels<br />test_0,193702 182294 158280 13977 306469<br /><span style=\"font-size: 14px; line-height: 1.4em;\">test_1,318312 306162 204337 189283 73127<br />test_2,298908 85549 168552 263457 287193<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">test_3,269079 116185 291325 14997 246482<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>\n",
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