Datasets:

Hyukkyu
/

superglue

	---
	annotations_creators:
	- other
	language_creators:
	- other
	multilinguality:
	- monolingual
	source_datasets:
	- original
	paperswithcode_id: superglue
	arxiv: 1905.00537
	pretty_name: SuperGLUE Benchmark Datasets
	tags:
	- superglue
	- nlp
	- benchmark
	license: mit
	language:
	- en
	dataset_info:
	- config_name: boolq
	features:
	- name: question
	dtype: string
	- name: passage
	dtype: string
	- name: idx
	dtype: int64
	- name: label
	dtype: bool
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	- name: train
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	num_examples: 9427
	- name: validation
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	num_examples: 3270
	- name: test
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	num_examples: 3245
	download_size: 6439045
	dataset_size: 10333940
	- config_name: cb
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: label
	dtype: string
	- name: idx
	dtype: int64
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	num_examples: 250
	- name: validation
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	num_examples: 56
	- name: test
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	num_examples: 250
	download_size: 137099
	dataset_size: 205831
	- config_name: copa
	features:
	- name: premise
	dtype: string
	- name: choice1
	dtype: string
	- name: choice2
	dtype: string
	- name: question
	dtype: string
	- name: label
	dtype: int64
	- name: idx
	dtype: int64
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	- name: train
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	num_examples: 400
	- name: validation
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	num_examples: 100
	- name: test
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	num_examples: 500
	download_size: 84158
	dataset_size: 125558
	- config_name: multirc
	features:
	- name: idx
	dtype: int64
	- name: version
	dtype: float64
	- name: passage
	struct:
	- name: questions
	list:
	- name: answers
	list:
	- name: idx
	dtype: int64
	- name: label
	dtype: int64
	- name: text
	dtype: string
	- name: idx
	dtype: int64
	- name: question
	dtype: string
	- name: text
	dtype: string
	splits:
	- name: train
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	num_examples: 456
	- name: validation
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	num_examples: 83
	- name: test
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	num_examples: 166
	download_size: 2053244
	dataset_size: 3681846
	- config_name: record
	features:
	- name: source
	dtype: string
	- name: passage
	struct:
	- name: entities
	list:
	- name: end
	dtype: int64
	- name: start
	dtype: int64
	- name: text
	dtype: string
	- name: qas
	list:
	- name: answers
	list:
	- name: end
	dtype: int64
	- name: start
	dtype: int64
	- name: text
	dtype: string
	- name: idx
	dtype: int64
	- name: query
	dtype: string
	- name: idx
	dtype: int64
	splits:
	- name: train
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	- name: validation
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	num_examples: 7481
	- name: test
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	num_examples: 7484
	download_size: 71256085
	dataset_size: 134477421
	- config_name: rte
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: label
	dtype: string
	- name: idx
	dtype: int64
	splits:
	- name: train
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	num_examples: 2490
	- name: validation
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	num_examples: 277
	- name: test
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	num_examples: 3000
	download_size: 1269005
	dataset_size: 1944967
	- config_name: wic
	features:
	- name: word
	dtype: string
	- name: sentence1
	dtype: string
	- name: sentence2
	dtype: string
	- name: idx
	dtype: int64
	- name: label
	dtype: bool
	- name: start1
	dtype: int64
	- name: start2
	dtype: int64
	- name: end1
	dtype: int64
	- name: end2
	dtype: int64
	- name: version
	dtype: float64
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	num_examples: 5428
	- name: validation
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	num_examples: 638
	- name: test
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	num_examples: 1400
	download_size: 591526
	dataset_size: 1069277
	- config_name: wsc
	features:
	- name: text
	dtype: string
	- name: target
	struct:
	- name: span1_index
	dtype: int64
	- name: span1_text
	dtype: string
	- name: span2_index
	dtype: int64
	- name: span2_text
	dtype: string
	- name: idx
	dtype: int64
	- name: label
	dtype: bool
	splits:
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	num_examples: 554
	- name: validation
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	num_examples: 104
	- name: test
	num_bytes: 32011
	num_examples: 146
	download_size: 47100
	dataset_size: 145558
	configs:
	- config_name: boolq
	data_files:
	- split: train
	path: boolq/train-*
	- split: validation
	path: boolq/validation-*
	- split: test
	path: boolq/test-*
	- config_name: cb
	data_files:
	- split: train
	path: cb/train-*
	- split: validation
	path: cb/validation-*
	- split: test
	path: cb/test-*
	- config_name: copa
	data_files:
	- split: train
	path: copa/train-*
	- split: validation
	path: copa/validation-*
	- split: test
	path: copa/test-*
	- config_name: multirc
	data_files:
	- split: train
	path: multirc/train-*
	- split: validation
	path: multirc/validation-*
	- split: test
	path: multirc/test-*
	- config_name: record
	data_files:
	- split: train
	path: record/train-*
	- split: validation
	path: record/validation-*
	- split: test
	path: record/test-*
	- config_name: rte
	data_files:
	- split: train
	path: rte/train-*
	- split: validation
	path: rte/validation-*
	- split: test
	path: rte/test-*
	- config_name: wic
	data_files:
	- split: train
	path: wic/train-*
	- split: validation
	path: wic/validation-*
	- split: test
	path: wic/test-*
	- config_name: wsc
	data_files:
	- split: train
	path: wsc/train-*
	- split: validation
	path: wsc/validation-*
	- split: test
	path: wsc/test-*
	---
	# SuperGLUE Benchmark Datasets

	This repository contains the [SuperGLUE](https://arxiv.org/pdf/1905.00537) benchmark datasets. Each dataset is available as a separate configuration, making it easy to load individual datasets using the [datasets](https://github.com/huggingface/datasets) library.

	## Dataset Descriptions

	### Datasets Included

	- BoolQ: A question-answering 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 paired with a paragraph from a Wikipedia article containing the answer.

	- CB (CommitmentBank): A natural language inference task where each example consists of a premise containing an embedded clause, and the corresponding hypothesis is the extraction of that clause. The task focuses on determining whether the premise entails the hypothesis, contradicts it, or is neutral.

	- COPA (Choice of Plausible Alternatives): A causal reasoning task where the system selects the more plausible alternative between two choices given a premise. Each example consists of a premise and two possible alternatives, and the task is to choose the alternative that has a causal relationship with the premise.

	- MultiRC (Multiple Sentence Reading Comprehension): A reading comprehension task where each example consists of a context paragraph, a question about the paragraph, and a list of possible answers. The task requires identifying all correct answers for each question, and there may be multiple correct answers.

	- ReCoRD (Reading Comprehension with Commonsense Reasoning Dataset): A cloze-style reading comprehension task that evaluates a model’s ability to use commonsense reasoning to predict which entity is missing from a passage. Each example consists of a passage with a missing entity and a list of possible entities to fill in the blank.

	- RTE (Recognizing Textual Entailment): A textual entailment task that involves determining whether a given premise entails a hypothesis. Each example consists of a premise and a hypothesis, and the task is to predict whether the hypothesis is true based on the premise.

	- WiC (Word-in-Context): A word sense disambiguation task that determines if a word is used in the same sense in two different contexts. Each example consists of two sentences containing the same word, and the task is to decide whether the word has the same meaning in both sentences.

	- WSC (Winograd Schema Challenge): A pronoun resolution task where the system must determine the antecedent of a pronoun in a sentence. Each example consists of a sentence with a pronoun and a list of possible antecedents, and the task is to select the correct antecedent.

	Each dataset has been preprocessed to ensure consistency across train, validation, and test splits. Missing keys in the test split have been filled with dummy values (type-aware) to match the features found in the training and validation splits.

	### Languages

	All data in SuperGLUE is in English.

	## Dataset Structure

	### Data Instances

	Each task in SuperGLUE is split into train, validation, and test sets. An example instance from each configuration is provided in the task-specific sections below.

	#### BoolQ
	- train split: 9427 examples
	- validation split: 3270 examples
	- test split: 3245 examples

	An example of 'train' looks as follows.
	```json
	{
	"question": "do iran and afghanistan speak the same language",
	"passage": "Persian language -- Persian (/\u02c8p\u025c\u02d0r\u0292\u0259n, -\u0283\u0259n/), also known by its endonym Farsi (\u0641\u0627\u0631\u0633\u06cc f\u0101rsi (f\u0252\u02d0\u027e\u02c8si\u02d0) ( listen)), is one of the Western Iranian languages within the Indo-Iranian branch of the Indo-European language family. It is primarily spoken in Iran, Afghanistan (officially known as Dari since 1958), and Tajikistan (officially known as Tajiki since the Soviet era), and some other regions which historically were Persianate societies and considered part of Greater Iran. It is written in the Persian alphabet, a modified variant of the Arabic script, which itself evolved from the Aramaic alphabet.",
	"idx": 0,
	"label": true
	}
	```

	#### CB
	- train split: 250 examples
	- validation split: 56 examples
	- test split: 250 examples

	An example of 'train' looks as follows.
	```json
	{
	"premise": "It was a complex language. Not written down but handed down. One might say it was peeled down.",
	"hypothesis": "the language was peeled down",
	"label": "entailment",
	"idx": 0
	}
	```

	#### COPA
	- train split: 400 examples
	- validation split: 100 examples
	- test split: 500 examples

	An example of 'train' looks as follows.
	```json
	{
	"premise": "My body cast a shadow over the grass.",
	"choice1": "The sun was rising.",
	"choice2": "The grass was cut.",
	"question": "cause",
	"label": 0,
	"idx": 0
	}
	```
	#### MultiRC
	- train split: 456 examples
	- validation split: 83 examples
	- test split: 166 examples

	An example of 'train' looks as follows.
	```json
	{
	"idx": 0,
	"version": 1.1,
	"passage": {
	"questions": [
	{
	"answers": [
	{
	"idx": 0,
	"label": 0,
	"text": "Children, Gerd, or Dorian Popa"
	},
	{
	"idx": 1,
	"label": 0,
	"text": "Monetary rewards"
	},
	{
	"idx": 2,
	"label": 1,
	"text": "Asking Pakistan to help the USA"
	},
	{
	"idx": 3,
	"label": 1,
	"text": "Meeting with General Musharraf"
	},
	{
	"idx": 4,
	"label": 1,
	"text": "President Clinton offered the moon in terms of better relations with the United States"
	},
	{
	"idx": 5,
	"label": 0,
	"text": "A Presidential visit in March"
	},
	{
	"idx": 6,
	"label": 1,
	"text": "Paper checks"
	},
	{
	"idx": 7,
	"label": 0,
	"text": "Increasing trade with Pakistan"
	},
	{
	"idx": 8,
	"label": 1,
	"text": "Persuading Pakistan to use its influence over the Taliban by dangling before him the possibility of a presidential visit in March as a reward for Pakistani cooperation"
	}
	],
	"idx": 0,
	"question": "What did the high-level effort to persuade Pakistan include?"
	},
	...
	],
	"text": "While this process moved along, diplomacy ..."
	}
	```

	#### ReCoRD
	- train split: 65709 examples
	- validation split: 7481 examples
	- test split: 7484 examples

	An example of 'train' looks as follows.
	```json
	{
	"source": "Daily mail",
	"passage": {
	"entities": [
	{
	"end": 96,
	"start": 86
	},
	{
	"end": 183,
	"start": 178
	},
	{
	"end": 206,
	"start": 197
	},
	{
	"end": 361,
	"start": 357
	},
	{
	"end": 539,
	"start": 535
	},
	{
	"end": 636,
	"start": 627
	},
	{
	"end": 681,
	"start": 672
	}
	],
	"text": "The harrowing stories of women and children locked up for so-called 'moral crimes' in Afghanistan's notorious female prison have been revealed after cameras were allowed inside. Mariam has been in Badam Bagh prison for three months after she shot a man who just raped her at gunpoint and then turned the weapon on herself - but she has yet to been charged. Nuria has eight months left to serve of her sentence for trying to divorce her husband. She gave birth in prison to her son and they share a cell together. Scroll down for video Nuria was jailed for trying to divorce her husband. Her son is one of 62 children living at Badam Bagh prison\n@highlight\nMost of the 202 Badam Bagh inmates are jailed for so-called 'moral crimes'\n@highlight\nCrimes include leaving their husbands or refusing an arrange marriage\n@highlight\n62 children live there and share cells with their mothers and five others"
	},
	"qas": [
	{
	"answers": [
	{
	"end": 539,
	"start": 535,
	"text": "Nuria"
	}
	],
	"idx": 0,
	"query": "The baby she gave birth to is her husbands and he has even offered to have the courts set her free if she returns, but @placeholder has refused."
	}
	],
	"idx": 0
	}
	```

	#### RTE
	- train split: 2490 examples
	- validation split: 277 examples
	- test split: 3000 examples

	An example of 'train' looks as follows.
	```json
	{
	"premise": "No Weapons of Mass Destruction Found in Iraq Yet.",
	"hypothesis": "Weapons of Mass Destruction Found in Iraq.",
	"label": "not_entailment",
	"idx": 0
	}
	```

	#### WiC
	- train split: 5428 examples
	- validation split: 638 examples
	- test split: 1400 examples

	An example of 'train' looks as follows.
	```json
	{
	"word": "place",
	"sentence1": "Do you want to come over to my place later?",
	"sentence2": "A political system with no place for the less prominent groups.",
	"idx": 0,
	"label": false,
	"start1": 31,
	"start2": 27,
	"end1": 36,
	"end2": 32,
	"version": 1.1
	}
	```

	#### WSC
	- train split: 554 examples
	- validation split: 104 examples
	- test split: 146 examples

	An example of 'train' looks as follows.
	```json
	{
	"text": "Mark told Pete many lies about himself, which Pete included in his book. He should have been more skeptical.",
	"target": {
	"span1_index": 0,
	"span1_text": "Mark",
	"span2_index": 13,
	"span2_text": "He"
	},
	"idx": 0,
	"label": false
	}
	```

	## Usage

	You can load any of the datasets using the Huggingface `datasets` library. For example, to load the BoolQ dataset, run:

	```python
	from datasets import load_dataset

	# Load the BoolQ dataset from the SuperGLUE benchmark
	dataset = load_dataset("Hyukkyu/superglue", "boolq")

	# Access train, validation, and test splits
	train_split = dataset["train"]
	validation_split = dataset["validation"]
	test_split = dataset["test"]

	print(train_split)
	```
	Replace "boolq" with the desired configuration name (e.g., "cb", "copa", "multirc", etc.) to load other datasets.

	## Data Preprocessing
	- Schema Consistency:
	A recursive procedure was used to infer the schema from the train and validation splits and fill in missing keys in the test split with dummy values. This ensures that all splits have the same features, preventing issues during model training or evaluation.
	- Type-Aware Dummy Values:
	Dummy values are inserted based on the expected type. For instance, missing boolean fields are filled with False, integer fields with -1, float fields with -1.0, and string fields with an empty string.

	## Citation
	```text
	@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},
	journal={Advances in neural information processing systems},
	volume={32},
	year={2019}
	}
	```