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README.md

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----
-license: mit
----
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+paperswithcode_id: commonsenseqa
+pretty_name: 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: 2207794
+    num_examples: 9741
+  - name: validation
+    num_bytes: 273848
+    num_examples: 1221
+  - name: test
+    num_bytes: 257842
+    num_examples: 1140
+  download_size: 1558570
+  dataset_size: 2739484
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+---
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset=load_dataset(
+	"Sadanto3933/commonsense_qa",
+	split="train",
+	)
+
+# ...
+```
+
+# Dataset Card for "commonsense_qa"
+
+## Dataset Description
+
+- **Homepage:** https://www.tau-nlp.org/commonsenseqa
+- **Repository:** https://github.com/jonathanherzig/commonsenseqa
+- **Paper:** https://arxiv.org/abs/1811.00937
+- **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:** 4.68 MB
+- **Size of the generated dataset:** 2.18 MB
+- **Total amount of disk used:** 6.86 MB
+
+### Dataset Summary
+
+CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
+to predict the correct 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 paper for details.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+The dataset is in English (`en`).
+
+## Dataset Structure
+
+### Data Instances
+
+
+An example of 'train' looks as follows:
+```json
+{'id': '075e483d21c29a511267ef62bedc0461',
+ 'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?',
+ 'question_concept': 'punishing',
+ 'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
+  'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']},
+ 'answerKey': 'A'}
+```
+
+### 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.
+
+## Dataset Creation
+
+
+### Licensing Information
+
+The dataset is licensed under the MIT License.
+
+See: https://github.com/jonathanherzig/commonsenseqa/issues/5
+
+### 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",
+}
+```