{
  "citation": "@article{choi2018quac,\n  title={Quac: Question answering in context},\n  author={Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke},\n  journal={arXiv preprint arXiv:1808.07036},\n  year={2018}\n}",
  "description": "Question Answering in Context is a dataset for modeling, understanding,\nand participating in information seeking dialog. Data instances consist\nof an interactive dialog between two crowd workers: (1) a student who poses\na sequence of freeform questions to learn as much as possible about a hidden\nWikipedia text, and (2) a teacher who answers the questions by providing\nshort excerpts (spans) from the text. QuAC introduces challenges not found\nin existing machine comprehension datasets: its questions are often more\nopen-ended, unanswerable, or only meaningful within the dialog context.",
  "downloadSize": "77043986",
  "location": {
    "urls": [
      "https://quac.ai/"
    ]
  },
  "name": "quac",
  "splits": [
    {
      "name": "train",
      "numBytes": "283625162",
      "shardLengths": [
        "20892",
        "20892",
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        "20892"
      ]
    },
    {
      "name": "validation",
      "numBytes": "28893010",
      "shardLengths": [
        "7354"
      ]
    }
  ],
  "supervisedKeys": {
    "input": "context",
    "output": "answers"
  },
  "version": "1.0.0"
}