{"quac":{"description":"Question Answering in Context (QuAC) is a dataset for modeling, understanding, and \nparticipating in information seeking dialog. Data instances consist of an interactive\ndialog between two crowd workers: (1) a student who poses a sequence of freeform\nquestions to learn as much as possible about a hidden Wikipedia text, and (2)\na teacher who answers the questions by providing short excerpts (spans) from the text.\n","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}\n","homepage":"https://quac.ai/","license":"","features":{"title":{"dtype":"string","id":null,"_type":"Value"},"section_title":{"dtype":"string","id":null,"_type":"Value"},"paragraph":{"dtype":"string","id":null,"_type":"Value"},"question":{"dtype":"string","id":null,"_type":"Value"},"answer":{"dtype":"string","id":null,"_type":"Value"}},"post_processed":null,"supervised_keys":null,"task_templates":null,"builder_name":"quac","config_name":"quac","version":{"version_str":"1.1.0","description":null,"major":1,"minor":1,"patch":0},"splits":{"train":{"name":"train","num_bytes":212391958,"num_examples":83568,"dataset_name":"quac"},"validation":{"name":"validation","num_bytes":20678483,"num_examples":7354,"dataset_name":"quac"}},"download_checksums":{"https://s3.amazonaws.com/my89public/quac/train_v0.2.json":{"num_bytes":68114819,"checksum":"ff5cca5a2e4b4d1cb5b5ced68b9fce88394ef6d93117426d6d4baafbcc05c56a"},"https://s3.amazonaws.com/my89public/quac/val_v0.2.json":{"num_bytes":8929167,"checksum":"09e622916280ba04c9352acb1bc5bbe80f11a2598f6f34e934c51d9e6570f378"}},"download_size":77043986,"post_processing_size":null,"dataset_size":233070441,"size_in_bytes":310114427}}
