{
  "fetched_at": "2026-10-20T05:50:59Z",
  "source_url": "https://www.kaggle.com/competitions/fit3181-5215/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<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, please follow the format of this file to submit your result</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>ID (test.csv)</strong> - unique id for a test question</li>\n<li><strong>Question (test.csv)</strong> - the question field</li>\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p>This is the home page of the competition. The competition is organized for FIT3181 and 5215: Deep Learning unit at Monash University. The students who are interested in can join in this competition.\n</p><p>If you are in the top 10, you will be awarded 5 points in your assignment 2, while if you are in the top 3, you will be awarded 10 points in your assignment 2.\n</p><p>Regarding the competition task, you can utilize any ML and DL techniques to train your model with the aim to obtain as highest as possible performance on a testing dataset. Below are some recommendations:\n</p><li> You can use the training and valid sets provided in assignment 2 to train and fine-tune your models. </li>\n- You can take advantage of pre-trained Word2Vec, Bert, and so on to boost up the performance.\n- You can apply boosting or ensembling techniques to improve performance.\n-  We do not restrict the techniques you apply. Be flexible and creative to propose your own way to train your models.\n<p></p><p>Please note that for those who join in this competition, in conjunction with your main assignment jupyter notebook file, you need to submit your trained model and a pdf document that describes how you train your model.\n</p><p>Hope that you enjoy this competition. Have funs!</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p></p><h2>Evaluation Metric</h2>\n<p>The evaluation metric for this competition is the accuracy </p><p>\n</p><h2>Submission Format</h2>\n<p><strong>Please follow the file sampleSumission.csv in the tab Data to submit your result.</strong></p>",
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}