
# 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://github.com/google-research-datasets/boolean-questions](https://github.com/google-research-datasets/boolean-questions)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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.

BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.

### 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

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

### Citation Information

```
@inproceedings{clark2019boolq,
  title={BoolQ: 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={NAACL},
  year={2019}
}
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}

Note that each SuperGLUE dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.

```


### 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:unknown superglue NLU natural language understanding region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "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"], "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}]}
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-unknown #superglue #NLU #natural language understanding #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:
* 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.


BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.


### 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


### 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.


BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy. ### 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 ### 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-unknown #superglue #NLU #natural language understanding #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.


BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy. ### 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 ### 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-unknown #superglue #NLU #natural language understanding #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.


BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.### 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. passage: #### 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