Datasets:
albertvillanova
HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#1)
210a4d2
metadata
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractive-qa
paperswithcode_id: drop
dataset_info:
features:
- name: section_id
dtype: string
- name: query_id
dtype: string
- name: passage
dtype: string
- name: question
dtype: string
- name: answers_spans
sequence:
- name: spans
dtype: string
- name: types
dtype: string
splits:
- name: train
num_bytes: 105572762
num_examples: 77400
- name: validation
num_bytes: 11737787
num_examples: 9535
download_size: 8308692
dataset_size: 117310549
Dataset Card for "drop"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://allennlp.org/drop
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 8.30 MB
- Size of the generated dataset: 110.91 MB
- Total amount of disk used: 119.21 MB
Dataset Summary
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 8.30 MB
- Size of the generated dataset: 110.91 MB
- Total amount of disk used: 119.21 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"answers_spans": {
"spans": ["Chaz Schilens"]
},
"passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...",
"question": "Who scored the first touchdown of the game?"
}
Data Fields
The data fields are the same among all splits.
default
passage: astringfeature.question: astringfeature.answers_spans: a dictionary feature containing:spans: astringfeature.
Data Splits
| name | train | validation |
|---|---|---|
| default | 77409 | 9536 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
Contributions
Thanks to @patrickvonplaten, @thomwolf, @mariamabarham, @lewtun for adding this dataset.