SQuAD

The Stanford Question Answering Dataset

What is SQuAD?

Stanford Question Answering Dataset (SQuAD) is a new 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. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets.

Explore SQuAD and model predictionsRead the paper (Rajpurkar et al. '16)

Getting Started

We've built a few resources to help you get started with the dataset.

Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):

To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use python evaluate-v1.1.py <path_to_dev-v1.1> <path_to_predictions>.

Once you have a built a model that works to your expectations on the dev set, you submit it to get official scores on the dev and a hidden test set. To preserve the integrity of test results, we do not release the test set to the public. Instead, we require you to submit your model so that we can run it on the test set for you. Here's a tutorial walking you through official evaluation of your model:

Submission Tutorial

Because SQuAD is an ongoing effort, we expect the dataset to evolve.

To keep up to date with major changes to the dataset, please subscribe:

Have Questions?

Ask us questions at our google group or at pranavsr@stanford.edu.

Star

Leaderboard

Since the release of our dataset, the community has made rapid progress! Here are the ExactMatch (EM) and F1 scores of the best models evaluated on the test set of v1.1. Will your model outperform humans on the QA task?

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar et al. '16)
82.30491.221

1

Jan 05, 2018
SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

1

Jan 03, 2018
r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

2

Dec 17, 2017
r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet
82.13688.126

2

Dec 22, 2017
AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

3

Nov 17, 2017
BiDAF + Self Attention + ELMo (ensemble)

Allen Institute for Artificial Intelligence

81.00387.432

4

Jan 13, 2018
SLQA+

single model

80.43687.021

4

Jan 04, 2018
{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

4

Jan 12, 2018
EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

5

Jan 03, 2018
r-net+ (single model)

Microsoft Research Asia

79.90186.536

6

Dec 05, 2017
SAN (ensemble model)

Microsoft Business AI Solutions Team

https://arxiv.org/pdf/1712.03556.pdf
79.60886.496

6

Dec 28, 2017
SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

7

Oct 17, 2017
Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

8

Oct 24, 2017
FusionNet (ensemble)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1711.07341
78.97886.016

9

Oct 22, 2017
DCN+ (ensemble)

Salesforce Research

78.85285.996

10

Nov 03, 2017
BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence

78.58085.833

11

Jan 02, 2018
Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504
78.43385.517

11

Nov 30, 2017
SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

12

Jan 03, 2018
MEMEN (single model)

Zhejiang University

https://arxiv.org/abs/1707.09098
78.23485.344

13

Jul 25, 2017
Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

14

Jan 10, 2018
MAMCN+ (single model)

Samsung Research

77.43685.130

15

Dec 06, 2017
AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

15

Aug 21, 2017
Reinforced Mnemonic Reader (ensemble)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798
77.67884.888

16

Dec 19, 2017
FRC (single model)

in review

76.24084.599

16

Dec 21, 2017
Jenga (ensemble)

Facebook AI Research

77.23784.466

16

Nov 06, 2017
Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504
76.99684.630

16

Dec 13, 2017
RaSoR + TR + LM (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609
77.58384.163

16

Nov 01, 2017
SAN (single model)

Microsoft Business AI Solutions Team

https://arxiv.org/pdf/1712.03556.pdf
76.82884.396

17

Oct 13, 2017
r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet
76.46184.265

18

Oct 22, 2017
Conductor-net (ensemble)

CMU

76.14683.991

19

Sep 08, 2017
FusionNet (single model)

Microsoft Business AI Solutions team

https://arxiv.org/abs/1711.07341
75.96883.900

19

Jul 14, 2017
smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

19

Oct 22, 2017
Interactive AoA Reader+ (single model)

Joint Laboratory of HIT and iFLYTEK

75.82183.843

20

Aug 18, 2017
RaSoR + TR (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609
75.78983.261

21

Oct 23, 2017
DCN+ (single model)

Salesforce Research

75.08783.081

22

Oct 31, 2017
SLQA (single model)

Alibaba iDST NLP

74.48982.815

22

Jul 10, 2017
DCN+ (single model)

Salesforce Research

74.86682.806

22

Nov 01, 2017
Mixed model (ensemble)

Sean

75.26582.769

23

Jan 02, 2018
Conductor-net (single model)

CMU

https://arxiv.org/abs/1710.10504
74.40582.742

23

May 21, 2017
MEMEN (ensemble)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1707.09098
75.37082.658

24

Mar 09, 2017
ReasoNet (ensemble)

MSR Redmond

https://arxiv.org/abs/1609.05284
75.03482.552

25

Oct 27, 2017
Unnamed submission by null

74.48982.312

25

Nov 17, 2017
two-attention-self-attention (ensemble)

guotong1988

75.22382.716

26

Jul 14, 2017
Mnemonic Reader (ensemble)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798
74.26882.371

27

Dec 23, 2017
S^3-Net (ensemble)

Kangwon National University in South Korea

74.12182.342

28

Jul 25, 2017
Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

28

Jul 29, 2017
SEDT (ensemble model)

CMU

https://arxiv.org/abs/1703.00572
74.09081.761

28

Aug 21, 2017
Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798
73.18881.816

28

Nov 06, 2017
Conductor-net (single)

CMU

https://arxiv.org/abs/1710.10504
73.24081.933

28

Dec 14, 2017
Jenga (single model)

Facebook AI Research

73.30381.754

28

Jul 06, 2017
SSAE (ensemble)

Tsinghua University

74.08081.665

29

Apr 22, 2017
SEDT+BiDAF (ensemble)

CMU

https://arxiv.org/abs/1703.00572
73.72381.530

29

Feb 22, 2017
BiDAF (ensemble)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603
73.74481.525

29

Jan 24, 2017
Multi-Perspective Matching (ensemble)

IBM Research

https://arxiv.org/abs/1612.04211
73.76581.257

29

May 01, 2017
jNet (ensemble)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617
73.01081.517

30

Oct 22, 2017
Conductor-net (single)

CMU

72.59081.415

30

Apr 12, 2017
T-gating (ensemble)

Peking University

72.75881.001

30

Nov 16, 2017
two-attention-self-attention (single model)

guotong1988

72.60081.011

30

Sep 20, 2017
BiDAF + Self Attention (single model)

Allen Institute for Artificial Intelligence

https://arxiv.org/abs/1710.10723
72.13981.048

31

Dec 15, 2017
S^3-Net (single model)

Kangwon National University in South Korea

71.90881.023

32

Nov 01, 2016
Dynamic Coattention Networks (ensemble)

Salesforce Research

https://arxiv.org/abs/1611.01604
71.62580.383

33

Jul 14, 2017
smarnet (single model)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1710.02772
71.41580.160

33

Apr 13, 2017
QFASE

NUS

71.89879.989

33

Nov 06, 2017
attention+self-attention (single model)

guotong1988

71.69880.462

34

Jul 14, 2017
Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798
70.99580.146

34

Oct 27, 2017
M-NET (single)

UFL

71.01679.835

35

Mar 24, 2017
jNet (single model)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617
70.60779.821

36

Mar 08, 2017
ReasoNet (single model)

MSR Redmond

https://arxiv.org/abs/1609.05284
70.55579.364

36

Mar 14, 2017
Document Reader (single model)

Facebook AI Research

https://arxiv.org/abs/1704.00051
70.73379.353

36

Dec 28, 2016
FastQAExt

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816
70.84978.857

37

Apr 14, 2017
Multi-Perspective Matching (single model)

IBM Research

https://arxiv.org/abs/1612.04211
70.38778.784

37

May 13, 2017
RaSoR (single model)

Google NY, Tel-Aviv University

https://arxiv.org/abs/1611.01436
70.84978.741

37

Apr 02, 2017
Ruminating Reader (single model)

New York University

https://arxiv.org/abs/1704.07415
70.63979.456

38

Aug 30, 2017
SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

39

Apr 12, 2017
SEDT+BiDAF (single model)

CMU

https://arxiv.org/abs/1703.00572
68.47877.971

40

Jun 25, 2017
PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

40

Dec 28, 2016
FastQA

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816
68.43677.070

40

Jul 29, 2017
SEDT (single model)

CMU

https://arxiv.org/abs/1703.00572
68.16377.527

40

Apr 12, 2017
T-gating (single model)

Peking University

68.13277.569

41

Nov 28, 2016
BiDAF (single model)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603
67.97477.323

42

Sep 19, 2017
AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/
67.61877.151

42

Oct 26, 2016
Match-LSTM with Ans-Ptr (Boundary) (ensemble)

Singapore Management University

https://arxiv.org/abs/1608.07905
67.90177.022

43

Feb 05, 2017
Iterative Co-attention Network

Fudan University

67.50276.786

44

Nov 01, 2016
Dynamic Coattention Networks (single model)

Salesforce Research

https://arxiv.org/abs/1611.01604
66.23375.896

44

Jan 03, 2018
newtest

single model

66.52775.787

45

Jan 03, 2018
baseline

single model

64.79674.272

46

Dec 09, 2017
Unnamed submission by ravioncodalab

64.43973.921

46

Oct 26, 2016
Match-LSTM with Bi-Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905
64.74473.743

47

Feb 19, 2017
Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

48

Nov 02, 2016
Fine-Grained Gating

Carnegie Mellon University

https://arxiv.org/abs/1611.01724
62.44673.327

48

Sep 21, 2017
OTF dict+spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286
64.08373.056

49

Sep 21, 2017
OTF spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286
62.89772.016

50

Sep 21, 2017
OTF spelling+lemma (single)

University of Montreal

https://arxiv.org/abs/1706.00286
62.60471.968

51

Sep 28, 2016
Dynamic Chunk Reader

IBM

https://arxiv.org/abs/1610.09996
62.49970.956

52

Aug 27, 2016
Match-LSTM with Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905
60.47470.695

53

Jan 05, 2018
PivRet (single model)

anonymous

58.76469.276

54

Aug 27, 2016
Match-LSTM with Ans-Ptr (Sentence)

Singapore Management University

https://arxiv.org/abs/1608.07905
54.50567.748