Dataset:
Tasks:
extractive-qa
Task Categories:
question-answering
Languages:
en
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Licenses:
cc-by-4.0
Annotations Creators:
crowdsourced
Source Datasets:
extended|wikipedia
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.
Supported Tasks
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
plain_text
- Size of downloaded dataset files: 33.51 MB
- Size of the generated dataset: 85.75 MB
- Total amount of disk used: 119.27 MB
An example of 'train' looks as follows.
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
Data Fields
The data fields are the same among all splits.
plain_text
id
: astring
feature.title
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
Data Splits Sample Size
name | train | validation |
---|---|---|
plain_text | 87599 | 10570 |
Dataset Creation
Curation Rationale
Source Data
Annotations
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
@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 @lewtun, @albertvillanova, @patrickvonplaten, @thomwolf for adding this dataset.
Homepage:
rajpurkar.github.io
Size of downloaded dataset files:
33.51 MB
Size of the generated dataset:
85.75 MB
Total amount of disk used:
119.27 MB
Models trained or fine-tuned on squad
Question Answering
•
Updated
•
120kdistilbert-base-uncased-distilled-squad
Question Answering
•
Updated
•
769k
Rachneet/t5-base-qg-hl-squadv2
Text2Text Generation
•
Updated
•
144
aychang/bert-large-cased-whole-word-masking-finetuned-squad
Question Answering
•
Updated

aychang/distilbert-squad
Question Answering
•
Updated

csarron/bert-base-uncased-squad-v1
Question Answering
•
Updated
•
270
csarron/mobilebert-uncased-squad-v1
Question Answering
•
Updated
•
56
csarron/roberta-base-squad-v1
Question Answering
•
Updated
•
1,890
lewtun/distilbert-base-uncased-distilled-squad-v1
Question Answering
•
Updated
madlag/bert-base-uncased-squad-v1-sparse0.25
Question Answering
•
Updated
madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1
Question Answering
•
Updated
madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1
Question Answering
•
Updated
•
44madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1
Question Answering
•
Updated
•
26madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1
Question Answering
•
Updated
madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1
Question Answering
•
Updated
•
39madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1
Question Answering
•
Updated
•
27madlag/bert-base-uncased-squadv1-x1.96-f88.3-d27-hybrid-filled-opt-v1
Question Answering
•
Updated
•
105madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1
Question Answering
•
Updated
•
168madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1
Question Answering
•
Updated
madlag/bert-base-uncased-squadv1-x2.44-f87.7-d26-hybrid-filled-v1
Question Answering
•
Updated
•
33
microsoft/prophetnet-large-uncased-squad-qg
Text2Text Generation
•
Updated
•
1,401mrm8488/RuPERTa-base-finetuned-squadv1
Question Answering
•
Updated
•
30mrm8488/mobilebert-uncased-finetuned-squadv1
Question Answering
•
Updated
•
66mrm8488/squeezebert-finetuned-squadv1
Question Answering
•
Updated
mrm8488/t5-base-finetuned-question-generation-ap
Text2Text Generation
•
Updated
•
5,806mrm8488/t5-small-finetuned-squadv1
Text2Text Generation
•
Updated

persiannlp/mt5-base-parsinlu-squad-reading-comprehension
Text2Text Generation
•
Updated

persiannlp/mt5-large-parsinlu-squad-reading-comprehension
Text2Text Generation
•
Updated
•
42
persiannlp/mt5-small-parsinlu-squad-reading-comprehension
Text2Text Generation
•
Updated
•
25
pierreguillou/bert-base-cased-squad-v1.1-portuguese
Question Answering
•
Updated
•
1,424
salti/bert-base-multilingual-cased-finetuned-squad
Question Answering
•
Updated
•
533
valhalla/bart-large-finetuned-squadv1
Question Answering
•
Updated
•
140
valhalla/distilt5-qa-qg-hl-12-6
Text2Text Generation
•
Updated
•
88
valhalla/distilt5-qa-qg-hl-6-4
Text2Text Generation
•
Updated
•
52
valhalla/distilt5-qg-hl-12-6
Text2Text Generation
•
Updated

valhalla/distilt5-qg-hl-6-4
Text2Text Generation
•
Updated
•
47
valhalla/t5-base-e2e-qg
Text2Text Generation
•
Updated
•
1,551
valhalla/t5-base-qa-qg-hl
Text2Text Generation
•
Updated
•
311k
valhalla/t5-base-qg-hl
Text2Text Generation
•
Updated
•
7,270
valhalla/t5-small-e2e-qg
Text2Text Generation
•
Updated
•
2,436
valhalla/t5-small-qa-qg-hl
Text2Text Generation
•
Updated
•
223k
valhalla/t5-small-qg-hl
Text2Text Generation
•
Updated
•
118k