{
  "fetched_at": "2026-10-20T12:52:13Z",
  "source_url": "https://www.kaggle.com/competitions/2020tmhw3/data",
  "pages": [
    {
      "name": "data-description",
      "content": "提供training data跟sample submission下載\n\ntest data就是1095文檔中，除了training data以外的都是\n\nFile descriptions training.txt - the training set 訓練資料\n\nhw3_sam.csv - a sample submission file in the correct format 上傳格式範本，Id代表docid，Value代表分到哪一類，這次作業分為13類，所以Value會介於1~13之間",
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      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "<p>2020 - NTU Course Introduction to text mining HW3</p>\n<p>multi-class classification using BERT embedding </p>\n<h2>注意事項</h2>\n<p>1.  對於隊名有規定，請大家使用學號！非學號者不予計分！\n另外，僅限一人一隊，請不要額外創小帳去增加測試次數。</p>\n\n<p>如果有kaggle使用上的問題或其他問題再寄信給我們詢問！</p>",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:</p>\n<hr>\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 author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids.&nbsp;For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
      "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 - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "hasIsPublished": false,
      "postTitle": "",
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}