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  "fetched_at": "2026-10-19T15:14:59Z",
  "source_url": "https://www.kaggle.com/competitions/explicit-content-detection/data",
  "pages": [
    {
      "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 - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "content": "<p>The data sets contain url and title of web pages. The goal is to detect porn content in a page.</p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - an id unique to a given web page</li>\n<li><strong>url</strong> - an url of a web page</li>\n<li><strong>title</strong> - a title of a web page</li>\n<li><strong>target</strong> - a web page content label</li>\n</ul>",
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      "name": "Description",
      "content": "<p>The goal of this competition is to develop a machine learning model for explicit content detection on a web page. In particular, it is proposed to develop a model for antiporn filter. Each web page is described by its url and title. The goal is to determine its content based on its description. </p>\n\n<p>Your final score for this competition will be estimated as follows:</br>\n1. «Score» = (\"your quality\"-\"baseline method quality\") / (\"max achieved quality\" - \"baseline method quality\")</br> \n2. Notebook with your best solution must be reproducible. Otherwise, you will not get any score.</p>",
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    {
      "name": "Evaluation",
      "content": "\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\n<p><strong>For every web page in the dataset</strong>, submission files should contain two columns: id and target.\n<p>The file should contain a header and have the following format:</p>\n<pre>id,target<br />135309,False<br /><span style=\"font-size: 14px; line-height: 1.4em;\">135320,True<br />135310,False<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">135311,False<br /></span><span style=\"font-size: 14px; line-height: 1.4em;\">etc.</span></pre>",
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