metadata

annotations_creators:
  - expert-generated
language_creators:
  - other
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other
task_categories:
  - text-classification
  - token-classification
  - question-answering
task_ids:
  - natural-language-inference
  - word-sense-disambiguation
  - coreference-resolution
  - extractive-qa
paperswithcode_id: superglue
pretty_name: SuperGLUE
tags:
  - superglue
  - NLU
  - natural language understanding
dataset_info:
  - config_name: boolq
    features:
      - name: question
        dtype: string
      - name: passage
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': 'False'
              '1': 'True'
    splits:
      - name: test
        num_bytes: 2107997
        num_examples: 3245
      - name: train
        num_bytes: 6179206
        num_examples: 9427
      - name: validation
        num_bytes: 2118505
        num_examples: 3270
    download_size: 4118001
    dataset_size: 10405708
  - config_name: cb
    features:
      - name: premise
        dtype: string
      - name: hypothesis
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': contradiction
              '2': neutral
    splits:
      - name: test
        num_bytes: 93660
        num_examples: 250
      - name: train
        num_bytes: 87218
        num_examples: 250
      - name: validation
        num_bytes: 21894
        num_examples: 56
    download_size: 75482
    dataset_size: 202772
  - config_name: copa
    features:
      - name: premise
        dtype: string
      - name: choice1
        dtype: string
      - name: choice2
        dtype: string
      - name: question
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': choice1
              '1': choice2
    splits:
      - name: test
        num_bytes: 60303
        num_examples: 500
      - name: train
        num_bytes: 49599
        num_examples: 400
      - name: validation
        num_bytes: 12586
        num_examples: 100
    download_size: 43986
    dataset_size: 122488
  - config_name: multirc
    features:
      - name: paragraph
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: idx
        struct:
          - name: paragraph
            dtype: int32
          - name: question
            dtype: int32
          - name: answer
            dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': 'False'
              '1': 'True'
    splits:
      - name: test
        num_bytes: 14996451
        num_examples: 9693
      - name: train
        num_bytes: 46213579
        num_examples: 27243
      - name: validation
        num_bytes: 7758918
        num_examples: 4848
    download_size: 1116225
    dataset_size: 68968948
  - config_name: record
    features:
      - name: passage
        dtype: string
      - name: query
        dtype: string
      - name: entities
        sequence: string
      - name: entity_spans
        sequence:
          - name: text
            dtype: string
          - name: start
            dtype: int32
          - name: end
            dtype: int32
      - name: answers
        sequence: string
      - name: idx
        struct:
          - name: passage
            dtype: int32
          - name: query
            dtype: int32
    splits:
      - name: train
        num_bytes: 179232052
        num_examples: 100730
      - name: validation
        num_bytes: 17479084
        num_examples: 10000
      - name: test
        num_bytes: 17200575
        num_examples: 10000
    download_size: 51757880
    dataset_size: 213911711
  - config_name: rte
    features:
      - name: premise
        dtype: string
      - name: hypothesis
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': not_entailment
    splits:
      - name: test
        num_bytes: 975799
        num_examples: 3000
      - name: train
        num_bytes: 848745
        num_examples: 2490
      - name: validation
        num_bytes: 90899
        num_examples: 277
    download_size: 750920
    dataset_size: 1915443
  - config_name: wic
    features:
      - name: word
        dtype: string
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: start1
        dtype: int32
      - name: start2
        dtype: int32
      - name: end1
        dtype: int32
      - name: end2
        dtype: int32
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': 'False'
              '1': 'True'
    splits:
      - name: test
        num_bytes: 180593
        num_examples: 1400
      - name: train
        num_bytes: 665183
        num_examples: 5428
      - name: validation
        num_bytes: 82623
        num_examples: 638
    download_size: 396213
    dataset_size: 928399
  - config_name: wsc
    features:
      - name: text
        dtype: string
      - name: span1_index
        dtype: int32
      - name: span2_index
        dtype: int32
      - name: span1_text
        dtype: string
      - name: span2_text
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': 'False'
              '1': 'True'
    splits:
      - name: test
        num_bytes: 31572
        num_examples: 146
      - name: train
        num_bytes: 89883
        num_examples: 554
      - name: validation
        num_bytes: 21637
        num_examples: 104
    download_size: 32751
    dataset_size: 143092
  - config_name: wsc.fixed
    features:
      - name: text
        dtype: string
      - name: span1_index
        dtype: int32
      - name: span2_index
        dtype: int32
      - name: span1_text
        dtype: string
      - name: span2_text
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': 'False'
              '1': 'True'
    splits:
      - name: test
        num_bytes: 31568
        num_examples: 146
      - name: train
        num_bytes: 89883
        num_examples: 554
      - name: validation
        num_bytes: 21637
        num_examples: 104
    download_size: 32751
    dataset_size: 143088
  - config_name: axb
    features:
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': not_entailment
    splits:
      - name: test
        num_bytes: 238392
        num_examples: 1104
    download_size: 33950
    dataset_size: 238392
  - config_name: axg
    features:
      - name: premise
        dtype: string
      - name: hypothesis
        dtype: string
      - name: idx
        dtype: int32
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': not_entailment
    splits:
      - name: test
        num_bytes: 53581
        num_examples: 356
    download_size: 10413
    dataset_size: 53581

Dataset Card for "super_glue"

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://super.gluebenchmark.com/
Repository: More Information Needed
Paper: https://arxiv.org/abs/1905.00537
Point of Contact: More Information Needed
Size of downloaded dataset files: 58.36 MB
Size of the generated dataset: 249.57 MB
Total amount of disk used: 307.94 MB

Dataset Summary

SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

axb

Size of downloaded dataset files: 0.03 MB
Size of the generated dataset: 0.24 MB
Total amount of disk used: 0.27 MB

An example of 'test' looks as follows.

axg

Size of downloaded dataset files: 0.01 MB
Size of the generated dataset: 0.05 MB
Total amount of disk used: 0.06 MB

An example of 'test' looks as follows.

boolq

Size of downloaded dataset files: 4.12 MB
Size of the generated dataset: 10.40 MB
Total amount of disk used: 14.52 MB

An example of 'train' looks as follows.

cb

Size of downloaded dataset files: 0.07 MB
Size of the generated dataset: 0.20 MB
Total amount of disk used: 0.28 MB

An example of 'train' looks as follows.

copa

Size of downloaded dataset files: 0.04 MB
Size of the generated dataset: 0.13 MB
Total amount of disk used: 0.17 MB

An example of 'train' looks as follows.

Data Fields

The data fields are the same among all splits.

axb

sentence1: a string feature.
sentence2: a string feature.
idx: a int32 feature.
label: a classification label, with possible values including entailment (0), not_entailment (1).

axg

premise: a string feature.
hypothesis: a string feature.
idx: a int32 feature.
label: a classification label, with possible values including entailment (0), not_entailment (1).

boolq

question: a string feature.
passage: a string feature.
idx: a int32 feature.
label: a classification label, with possible values including False (0), True (1).

cb

premise: a string feature.
hypothesis: a string feature.
idx: a int32 feature.
label: a classification label, with possible values including entailment (0), contradiction (1), neutral (2).

copa

premise: a string feature.
choice1: a string feature.
choice2: a string feature.
question: a string feature.
idx: a int32 feature.
label: a classification label, with possible values including choice1 (0), choice2 (1).

Data Splits

axb

	test
axb	1104

axg

	test
axg	356

boolq

	train	validation	test
boolq	9427	3270	3245

cb

	train	validation	test
cb	250	56	250

copa

	train	validation	test
copa	400	100	500

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The primary SuperGLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset, but it is our understanding that these licenses allow for their use and redistribution in a research context.

Citation Information

If you use SuperGLUE, please cite all the datasets you use in any papers that come out of your work. In addition, we encourage you to use the following BibTeX citation for SuperGLUE itself:

@article{wang2019superglue,
  title={Super{GLUE}: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Alex Wang and Yada Pruksachatkun and Nikita Nangia and Amanpreet Singh and Julian Michael and Felix Hill and Omer Levy and Samuel R. Bowman},
  journal={arXiv preprint 1905.00537},
  year={2019}
}
@inproceedings{clark2019boolq,
  title={{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions},
  author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei and Kwiatkowski, Tom and Collins, Michael and Toutanova, Kristina},
  booktitle={Proceedings of NAACL-HLT 2019},
  year={2019}
}
@inproceedings{demarneffe:cb,
  title={{The CommitmentBank}: Investigating projection in naturally occurring discourse},
  author={De Marneffe, Marie-Catherine and Simons, Mandy and Tonhauser, Judith},
  note={To appear in proceedings of Sinn und Bedeutung 23. Data can be found at https://github.com/mcdm/CommitmentBank/},
  year={2019}
}
@inproceedings{roemmele2011choice,
  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S.},
  booktitle={2011 AAAI Spring Symposium Series},
  year={2011}
}
@inproceedings{khashabi2018looking,
  title={Looking beyond the surface: A challenge set for reading comprehension over multiple sentences},
  author={Khashabi, Daniel and Chaturvedi, Snigdha and Roth, Michael and Upadhyay, Shyam and Roth, Dan},
  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  pages={252--262},
  year={2018}
}
@article{zhang2018record,
  title={{ReCoRD}: Bridging the Gap between Human and Machine Commonsense Reading Comprehension},
  author={Sheng Zhang and Xiaodong Liu and Jingjing Liu and Jianfeng Gao and Kevin Duh and Benjamin Van Durme},
  journal={arXiv preprint 1810.12885},
  year={2018}
}
@incollection{dagan2006pascal,
  title={The {PASCAL} recognising textual entailment challenge},
  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
  booktitle={Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment},
  pages={177--190},
  year={2006},
  publisher={Springer}
}
@article{bar2006second,
  title={The second {PASCAL} recognising textual entailment challenge},
  author={Bar Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
  year={2006}
}
@inproceedings{giampiccolo2007third,
  title={The third {PASCAL} recognizing textual entailment challenge},
  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
  pages={1--9},
  year={2007},
  organization={Association for Computational Linguistics},
}
@article{bentivogli2009fifth,
  title={The Fifth {PASCAL} Recognizing Textual Entailment Challenge},
  author={Bentivogli, Luisa and Dagan, Ido and Dang, Hoa Trang and Giampiccolo, Danilo and Magnini, Bernardo},
  booktitle={TAC},
  year={2009}
}
@inproceedings{pilehvar2018wic,
  title={{WiC}: The Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations},
  author={Pilehvar, Mohammad Taher and Camacho-Collados, Jose},
  booktitle={Proceedings of NAACL-HLT},
  year={2019}
}
@inproceedings{rudinger2018winogender,
  title={Gender Bias in Coreference Resolution},
  author={Rudinger, Rachel  and  Naradowsky, Jason  and  Leonard, Brian  and  {Van Durme}, Benjamin},
  booktitle={Proceedings of NAACL-HLT},
  year={2018}
}
@inproceedings{poliak2018dnc,
  title={Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation},
  author={Poliak, Adam and Haldar, Aparajita and Rudinger, Rachel and Hu, J. Edward and Pavlick, Ellie and White, Aaron Steven and {Van Durme}, Benjamin},
  booktitle={Proceedings of EMNLP},
  year={2018}
}
@inproceedings{levesque2011winograd,
  title={The {W}inograd schema challenge},
  author={Levesque, Hector J and Davis, Ernest and Morgenstern, Leora},
  booktitle={{AAAI} Spring Symposium: Logical Formalizations of Commonsense Reasoning},
  volume={46},
  pages={47},
  year={2011}
}

Contributions

Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

Dataset Card for "super_glue"

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

axb

axg

boolq

cb

copa

Data Fields

axb

axg

boolq

cb

copa

Data Splits

axb

axg

boolq

cb

copa

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

Contributions