
# Dataset Card for "super_glue"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://super.gluebenchmark.com/
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://arxiv.org/abs/1905.00537
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 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](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
task_categories:text-classification task_categories:token-classification task_categories:question-answering task_ids:natural-language-inference task_ids:word-sense-disambiguation task_ids:coreference-resolution task_ids:extractive-qa annotations_creators:expert-generated language_creators:other multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|other language:en license:other superglue NLU natural language understanding arxiv:1905.00537 region:us
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"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": 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"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, 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TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-question-answering #task_ids-natural-language-inference #task_ids-word-sense-disambiguation #task_ids-coreference-resolution #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other #language-English #license-other #superglue #NLU #natural language understanding #arxiv-1905.00537 #region-us 

Dataset Card for "super\_glue"
==============================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* 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
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper: URL
* Point of Contact:
* 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 (URL 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


### Languages


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



#### 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


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.


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:


### Contributions


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



### Dataset Summary


SuperGLUE (URL 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 ### Languages


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 #### 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


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.


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: ### Contributions


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-question-answering #task_ids-natural-language-inference #task_ids-word-sense-disambiguation #task_ids-coreference-resolution #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other #language-English #license-other #superglue #NLU #natural language understanding #arxiv-1905.00537 #region-us 
 ### Dataset Summary


SuperGLUE (URL 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 ### Languages


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 #### 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


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.


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: ### Contributions


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_categories-token-classification #task_categories-question-answering #task_ids-natural-language-inference #task_ids-word-sense-disambiguation #task_ids-coreference-resolution #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other #language-English #license-other #superglue #NLU #natural language understanding #arxiv-1905.00537 #region-us 
### Dataset Summary


SuperGLUE (URL 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### Languages


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. passage: ### 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#### 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