squad
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All datasets matching “squad”squad
Dataset Card for SQuAD
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.
Supported Tasks and Leaderboards
Question… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.squad_v2
Dataset Card for SQuAD 2.0
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.qa_squadshifts_synthetic_randomTBA
custom_squadStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.SQuADDS_Layouts
SQuADDS Layouts - versioned GDS artifacts for superconducting quantum hardware
SQuADDS Layouts is the geometry-artifact companion to
SQuADDS_DB, the
Superconducting Qubit And Device Design and Simulation Database. It provides
checksum-verified GDS files, stable geometry identities, and machine-readable
geometry metadata so a simulation result can be traced to the exact layout
that produced it.
Homepage: https://lfl-lab.github.io/SQuADDS/
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/SQuADDS/SQuADDS_Layouts.based-squadPlease consider citing the following if you use this task in your work:
@inproceedings{Rajpurkar2018SQuAD2,
title={Know What You Don't Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Jian Zhang and Percy Liang},
booktitle={ACL 2018},
year={2018}
}
@article{arora2024simple,
title={Simple linear attention language models balance the recall-throughput tradeoff},
author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas… See the full description on the dataset page: https://huggingface.co/datasets/hazyresearch/based-squad.