squad

  • Description:

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.

@article{2016arXiv160605250R,
       author = { {Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}

squad/v1.1 (default config)

  • Config description: Version 1.1.0 of SQUAD

  • Download size: 33.51 MiB

  • Dataset size: 94.04 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 87,599
'validation' 10,570
  • Features:
FeaturesDict({
    'answers': Sequence({
        'answer_start': tf.int32,
        'text': Text(shape=(), dtype=tf.string),
    }),
    'context': Text(shape=(), dtype=tf.string),
    'id': tf.string,
    'question': Text(shape=(), dtype=tf.string),
    'title': Text(shape=(), dtype=tf.string),
})

squad/v2.0

  • Config description: Version 2.0.0 of SQUAD

  • Download size: 44.34 MiB

  • Dataset size: 148.51 MiB

  • Auto-cached (documentation): Yes (validation), Only when shuffle_files=False (train)

  • Splits:

Split Examples
'train' 130,319
'validation' 11,873
  • Features:
FeaturesDict({
    'answers': Sequence({
        'answer_start': tf.int32,
        'text': Text(shape=(), dtype=tf.string),
    }),
    'context': Text(shape=(), dtype=tf.string),
    'id': tf.string,
    'is_impossible': tf.bool,
    'plausible_answers': Sequence({
        'answer_start': tf.int32,
        'text': Text(shape=(), dtype=tf.string),
    }),
    'question': Text(shape=(), dtype=tf.string),
    'title': Text(shape=(), dtype=tf.string),
})