README.md

metadata

dataset_info:
  features:
    - name: context
      dtype: string
    - name: questions
      dtype: string
  splits:
    - name: train
      num_bytes: 20293805
      num_examples: 18896
    - name: validation
      num_bytes: 2376313
      num_examples: 2067
  download_size: 12600387
  dataset_size: 22670118
annotations_creators:
  - crowdsourced
language:
  - en
language_creators:
  - crowdsourced
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: Question Generation for T5 based on Squad V1.1
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|squad
tags:
  - questiongeneration
  - question-generation
  - text2text-generation
task_categories:
  - text2text-generation
task_ids: []

Dataset Card for "squad-v1.1-t5-question-generation"

Dataset Description

• Homepage: https://rajpurkar.github.io/SQuAD-explorer/
• Paper: SQuAD: 100,000+ Questions for Machine Comprehension of Text

Dataset Summary

This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation specifically for the T5 family of models. The prefix is generate questions: so that the task can be unique to a trained model.

Check out the generation notebook here.

Supported Tasks and Leaderboards

More Information Needed

Languages

Dataset Structure

Data Instances

plain_text

An example of 'train' looks as follows.

{
    "context": "generate questions: This is a test context.",
    "question": "Is this a test? {sep_token} Is this another Test {sep_token}"
}

Data Fields

The data fields are the same among all splits.

plain_text

• context: a string feature.
• question: a string feature.

Data Splits

name	train	validation
plain_text	18896	2067

Citation Information

@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},
}

Contributions

Thanks to Derek Thomas and Thomas Simonini for adding this to the hub

Check out: How to contribute more

Visitors