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  "fetched_at": "2026-10-20T02:45:04Z",
  "source_url": "https://www.kaggle.com/competitions/dsaa-6100-fine-tuning-llm/data",
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    {
      "name": "data-description",
      "content": "## Files\n\n* **train_data.csv** - The dataset for training your model.\n* **test_data.csv** - The dataset on which you'll make your predictions.\n\n## Columns\n\n* `ID` - A unique identifier for each text entry.\n* `Output Content` - The content generated by the model. This is what you'll be predicting in the test set.\n* `Input Context` - The context or prompt based on which the content was generated. This might be present in the training data to provide context for the generated content.\n",
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      "name": "Evaluation",
      "content": "Submissions are evaluated based on the quality of the generated text compared to the original content. We primarily use the BLEU score and Jaccard similarity to measure the coherence, relevance, and fluency of the generated content.\n\n## Submission File\n\nFor each ID in the test set, you must generate a corresponding text content. The file should contain a header and have the following format:\n\n\nId,label\n1,\"Generated content for ID 1...\"\n2,\"Generated content for ID 2...\"\n3,\"Generated content for ID 3...\"\netc.\n\n\nPlease ensure that the generated content is relevant and closely aligned with the context of the given dataset.\n",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "name": "abstract",
      "content": "Welcome to the Text Generation Evaluation Challenge! In this competition, participants are tasked with fine-tuning and deploying state-of-the-art language models to generate human-like text content based on the given context.\n\nThe world of Natural Language Processing (NLP) has witnessed rapid advancements, especially in the domain of text generation. This competition aims to push the boundaries further, evaluating how closely machine-generated content can resemble human-like textual patterns and fluency.\n\nFor newcomers to Kaggle, all content here is written in markdown. If you're transferring information from other platforms like Word or a browser, ensure that the markdown or HTML is cleaned up for an optimal display.\n\n\n### Goal:\nThe primary goal of this competition is to harness the capabilities of modern language models and produce text that's as coherent, relevant, and fluent as human-generated content. Participants will be evaluated on the quality of their generated text in comparison to the original content using metrics like the BLEU score and Jaccard similarity.\n",
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