{
  "fetched_at": "2026-10-19T16:13:59Z",
  "source_url": "https://www.kaggle.com/competitions/amazon-pet-product-reviews-classification/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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    {
      "name": "data-description",
      "content": "You are provided with labeled Amazon product reviews. Each review consists of label and text. The task is to predict the label (dogs, fish aquatic pets, cats, birds, bunny rabbit central or small animals).\n\nFile descriptions\n\n- **train.csv** - the training set\n- **test.csv** - the test set\n- **valid.csv** - the validation set\n- **unlabeled.csv** - additional reviews to improve your models",
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    {
      "name": "Description",
      "content": "This competition is designed to explore various semi-supervised and transfer learning approaches to NLP, namely, to text classification. \n\nHere you're going to classify Amazon pet product reviews. Do resort to unlabeled data to improve classification. Try various SotA approaches: ULMFiT, BERT fine-tuning etc. \n\nPlease share your findings via Kernels and Discussions. \n",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is **micro-averaged of F1-Score**. \n\n## Submission Format\nFor every review in the dataset, submission files should contain two columns: **id** and **label**. Label should be one of the following: dogs, fish aquatic pets, cats, birds, bunny rabbit central or small animals. See **sample_submission.csv**",
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}