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 and development sets of v1.1. Will your model outperform humans on the QA task?

RankModelEMF1

1

Oct 17, 2017
Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

2

Oct 24, 2017
FusionNet (ensemble)

Microsoft Business AI Solutions Team

78.97886.016

3

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

Allen Institute for Artificial Intelligence

78.58085.833

3

Oct 12, 2017
r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet
78.92685.722

3

Oct 22, 2017
DCN+ (ensemble)

Salesforce Research

78.85285.996

4

Oct 22, 2017
BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence

77.85685.344

5

Jul 25, 2017
Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

6

Aug 21, 2017
Reinforced Mnemonic Reader (ensemble)

NUDT and Fudan University

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

7

Nov 03, 2017
SLQA(ensemble model)

Alibaba iDST NLP

77.53184.803

8

Nov 06, 2017
Conductor-net (ensemble)

CMU

76.99684.630

9

Nov 01, 2017
SAN (single model)

Microsoft Business AI Solutions Team

76.82884.396

10

Oct 13, 2017
r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet
76.46184.265

11

Oct 22, 2017
Conductor-net (ensemble)

CMU

76.14683.991

12

Jul 14, 2017
smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

12

Sep 08, 2017
AIR-FusionNet (single model)

Microsoft Business AI Solutions team

75.96883.900

13

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

Joint Laboratory of HIT and iFLYTEK

75.82183.843

14

Aug 18, 2017
Reg-RaSoR (single model)

Google NY, Tel-Aviv University

75.78983.261

15

Oct 23, 2017
DCN+ (single model)

Salesforce Research

75.08783.081

16

Jul 10, 2017
DCN+ (single model)

Salesforce Research

74.86682.806

16

May 21, 2017
MEMEN (ensemble)

Eigen Technology & Zhejiang University

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

17

Mar 09, 2017
ReasoNet (ensemble)

MSR Redmond

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

17

Nov 01, 2017
Mixed model (ensemble)

Sean

75.26582.769

18

Jul 14, 2017
Mnemonic Reader (ensemble)

NUDT and Fudan University

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

18

Oct 18, 2017
Unnamed submission by eddielin

74.48982.312

19

Jul 25, 2017
Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

20

Aug 21, 2017
Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

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

20

Jul 29, 2017
SEDT (ensemble model)

CMU

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

20

Nov 06, 2017
Conductor-net (single)

CMU

73.24081.933

20

Jul 06, 2017
SSAE (ensemble)

Tsinghua University

74.08081.665

21

Apr 22, 2017
SEDT+BiDAF (ensemble)

CMU

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

21

Feb 22, 2017
BiDAF (ensemble)

Allen Institute for AI & University of Washington

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

22

May 01, 2017
jNet (ensemble)

USTC & National Research Council Canada & York University

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

23

Oct 22, 2017
Conductor-net (single)

CMU

72.59081.415

23

Jan 24, 2017
Multi-Perspective Matching (ensemble)

IBM Research

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

24

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

Allen Institute for Artificial Intelligence

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

24

Apr 12, 2017
T-gating (ensemble)

Peking University

72.75881.001

25

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

guotong1988

71.69880.462

26

Nov 01, 2016
Dynamic Coattention Networks (ensemble)

Salesforce Research

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

26

Apr 13, 2017
QFASE

NUS

71.89879.989

26

Jul 14, 2017
smarnet (single model)

Eigen Technology & Zhejiang University

71.41580.160

27

Jul 14, 2017
Mnemonic Reader (single model)

NUDT and Fudan University

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

27

Oct 27, 2017
M-NET (single)

UFL

71.01679.835

28

Apr 02, 2017
Ruminating Reader (single model)

New York University

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

29

Mar 08, 2017
ReasoNet (single model)

MSR Redmond

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

29

Mar 14, 2017
Document Reader (single model)

Facebook AI Research

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

29

Mar 24, 2017
jNet (single model)

USTC & National Research Council Canada & York University

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

29

May 13, 2017
RaSoR (single model)

Google NY, Tel-Aviv University

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

29

Dec 28, 2016
FastQAExt

German Research Center for Artificial Intelligence

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

30

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

IBM Research

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

31

Aug 30, 2017
SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

32

Apr 12, 2017
SEDT+BiDAF (single model)

CMU

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

33

Jun 25, 2017
PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

34

Apr 12, 2017
T-gating (single model)

Peking University

68.13277.569

34

Jul 29, 2017
SEDT (single model)

CMU

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

34

Dec 28, 2016
FastQA

German Research Center for Artificial Intelligence

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

34

Nov 28, 2016
BiDAF (single model)

Allen Institute for AI & University of Washington

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

35

Sep 19, 2017
AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/
67.61877.151

35

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

Singapore Management University

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

36

Feb 05, 2017
Iterative Co-attention Network

Fudan University

67.50276.786

37

Nov 01, 2016
Dynamic Coattention Networks (single model)

Salesforce Research

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

38

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

Singapore Management University

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

39

Feb 19, 2017
Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

40

Nov 02, 2016
Fine-Grained Gating

Carnegie Mellon University

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

40

Sep 21, 2017
OTF dict+spelling (single)

University of Montreal

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

41

Sep 21, 2017
OTF spelling (single)

University of Montreal

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

42

Sep 21, 2017
OTF spelling+lemma (single)

University of Montreal

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

43

Sep 28, 2016
Dynamic Chunk Reader

IBM

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

44

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

Singapore Management University

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

45

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

Singapore Management University

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

Stanford University

(Rajpurkar et al. '16)
82.30491.221