README.md

---
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
  splits:
  - name: train
    num_bytes: 6136774
    num_examples: 9427
  - name: validation
    num_bytes: 2103781
    num_examples: 3270
  - name: test
    num_bytes: 2093385
    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
  splits:
  - name: train
    num_bytes: 89859
    num_examples: 250
  - name: validation
    num_bytes: 22480
    num_examples: 56
  - name: test
    num_bytes: 93492
    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
  splits:
  - name: train
    num_bytes: 50833
    num_examples: 400
  - name: validation
    num_bytes: 12879
    num_examples: 100
  - name: test
    num_bytes: 61846
    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
    num_bytes: 2393721
    num_examples: 456
  - name: validation
    num_bytes: 429255
    num_examples: 83
  - name: test
    num_bytes: 858870
    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
    num_bytes: 110591940
    num_examples: 65709
  - name: validation
    num_bytes: 12375907
    num_examples: 7481
  - name: test
    num_bytes: 11509574
    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
    num_bytes: 877041
    num_examples: 2490
  - name: validation
    num_bytes: 94010
    num_examples: 277
  - name: test
    num_bytes: 973916
    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
  splits:
  - name: train
    num_bytes: 767620
    num_examples: 5428
  - name: validation
    num_bytes: 94651
    num_examples: 638
  - name: test
    num_bytes: 207006
    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:
  - name: train
    num_bytes: 91597
    num_examples: 554
  - name: validation
    num_bytes: 21950
    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}
}
```