---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- id
license:
- mit
multilinguality:
- monolingual
pretty_name: CommonsenseQA-ID
size_categories:
- 1K<n<10K
source_datasets:
- machine-translation
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: commonsenseqa
dataset_info:
  features:
  - name: id
    dtype: string
  - name: question
    dtype: string
  - name: question_concept
    dtype: string
  - name: choices
    sequence:
    - name: label
      dtype: string
    - name: text
      dtype: string
  - name: answerKey
    dtype: string
  splits:
  - name: train
    num_bytes: 2209044
    num_examples: 9741
  - name: validation
    num_bytes: 274033
    num_examples: 1221
  - name: test
    num_bytes: 258017
    num_examples: 1140
  download_size: 4680691
  dataset_size: 2741094
---

# Dataset Card for "commonsense_qa-ID"

## Dataset Description

- **Homepage:** https://github.com/rizquuula/commonsense_qa-ID
- **Repository:** https://github.com/rizquuula/commonsense_qa-ID

### Dataset Summary

CommonsenseQA-ID is Indonesian translation version of CommonsenseQA, translated using Google Translation API v2/v3 Basic, all code used for the translation process available in our public repository.

CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see original paper for details.

### Languages

The dataset is in Indonesian (`id`).

## Dataset Structure

### Data Instances

#### default

- **Size of downloaded dataset files:** 4.68 MB
- **Size of the generated dataset:** 2.18 MB
- **Total amount of disk used:** 6.86 MB

An example of 'train' looks as follows:
```
{
  'id': '61fe6e879ff18686d7552425a36344c8',
  'question': 'Sammy ingin pergi ke tempat orang-orang itu berada. Ke mana dia bisa pergi?',
  'question_concept': 'rakyat',
  'choices': {
    'label': ['A', 'B', 'C', 'D', 'E'],
    'text': ['trek balap', 'daerah berpenduduk', 'gurun pasir', 'Apartemen', 'penghalang jalan']
  },
  'answerKey': 'B'
}
```

### Data Fields

The data fields are the same among all splits.

#### default
- `id` (`str`): Unique ID.
- `question`: a `string` feature.
- `question_concept` (`str`): ConceptNet concept associated to the question.
- `choices`: a dictionary feature containing:
  - `label`: a `string` feature.
  - `text`: a `string` feature.
- `answerKey`: a `string` feature.

### Data Splits

| name    | train | validation | test |
|---------|------:|-----------:|-----:|
| default |  9741 |       1221 | 1140 |

### Licensing Information

The dataset is licensed under the MIT License.

### Citation Information

```
@inproceedings{talmor-etal-2019-commonsenseqa,
    title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
    author = "Talmor, Alon  and
      Herzig, Jonathan  and
      Lourie, Nicholas  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1421",
    doi = "10.18653/v1/N19-1421",
    pages = "4149--4158",
    archivePrefix = "arXiv",
    eprint        = "1811.00937",
    primaryClass  = "cs",
}
```