{
  "fetched_at": "2026-10-24T18:16:48Z",
  "source_url": "https://www.kaggle.com/competitions/colac-test-evaluation/data",
  "pages": [
    {
      "name": "data-description",
      "content": "",
      "isPublished": false,
      "hasIsPublished": false,
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    {
      "name": "Description",
      "content": "Public evaluation of Corpus of Linguistic Acceptability in Chinese (CoLAC) test set. \n\nThere are two sets of labels in the training and development data: label0 (linguist label) and label1 (crowd label). Label1 is preferred, but you may be creative and make use of label0. Refer to the [paper](https://arxiv.org/abs/2305.14091) for more details.\n\nPlease cite us as:\n```bibtex\n@article{hu2023revisiting,\n      title={Revisiting Acceptability Judgements},\n      author={Hai Hu and Ziyin Zhang and Weifang Huang and Jackie Yan-Ki Lai and Aini Li and Yina Ma and Jiahui Huang and Peng Zhang and Rui Wang},\n      year={2023},\n      eprint={2305.14091},\n      journal={CoRR},\n      volume={abs/2305.14091},\n      url={http://arxiv.org/abs/2305.14091},\n      eprinttype={arXiv},\n}\n```",
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    {
      "name": "Evaluation",
      "content": "CoLAC is a binary classification task and uses Matthew's Correlation Coefficient as the evaluation metric. A random prediction will receive a score around 0, and negative scores are possible if your performance is worse than random.\n\nTraining, development, and unlabeled test data can be downloaded in **Data**. To receive your test result, submit a CSV file containing two columns: 'id' (in the same order as `test.csv`, and 'label' (either 0 or 1)). We provide a random sample submission file.",
      "isPublished": false,
      "hasIsPublished": false,
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    },
    {
      "name": "rules",
      "content": "At most two submissions per day.",
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}