Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
Commit
•
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Parent(s):
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Update parquet files
Browse files- .gitattributes +0 -27
- README.md +0 -225
- cosmos_qa.py +0 -116
- dataset_infos.json +0 -1
- default/cosmos_qa-test.parquet +3 -0
- default/cosmos_qa-train.parquet +3 -0
- default/cosmos_qa-validation.parquet +3 -0
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README.md
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---
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annotations_creators:
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- crowdsourced
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language:
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- en
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language_creators:
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- found
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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pretty_name: CosmosQA
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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task_categories:
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- multiple-choice
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task_ids:
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- multiple-choice-qa
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paperswithcode_id: cosmosqa
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: context
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dtype: string
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- name: question
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dtype: string
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- name: answer0
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dtype: string
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- name: answer1
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dtype: string
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- name: answer2
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dtype: string
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- name: answer3
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dtype: string
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- name: label
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dtype: int32
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splits:
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- name: train
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num_bytes: 17159918
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num_examples: 25262
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- name: test
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num_bytes: 5121479
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num_examples: 6963
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- name: validation
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num_bytes: 2186987
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num_examples: 2985
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download_size: 24399475
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dataset_size: 24468384
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---
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# Dataset Card for "cosmos_qa"
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** [https://wilburone.github.io/cosmos/](https://wilburone.github.io/cosmos/)
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- **Repository:** https://github.com/wilburOne/cosmosqa/
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- **Paper:** [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277)
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- **Point of Contact:** [Lifu Huang](mailto:warrior.fu@gmail.com)
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- **Size of downloaded dataset files:** 23.27 MB
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- **Size of the generated dataset:** 23.37 MB
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- **Total amount of disk used:** 46.64 MB
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### Dataset Summary
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Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
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### Supported Tasks and Leaderboards
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Languages
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Dataset Structure
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### Data Instances
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#### default
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- **Size of downloaded dataset files:** 23.27 MB
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- **Size of the generated dataset:** 23.37 MB
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- **Total amount of disk used:** 46.64 MB
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An example of 'validation' looks as follows.
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```
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This example was too long and was cropped:
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{
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"answer0": "If he gets married in the church he wo nt have to get a divorce .",
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"answer1": "He wants to get married to a different person .",
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"answer2": "He wants to know if he does nt like this girl can he divorce her ?",
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"answer3": "None of the above choices .",
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"context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
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"id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
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"label": 1,
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"question": "Why is this person asking about divorce ?"
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}
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```
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### Data Fields
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The data fields are the same among all splits.
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#### default
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- `id`: a `string` feature.
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- `context`: a `string` feature.
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- `question`: a `string` feature.
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- `answer0`: a `string` feature.
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- `answer1`: a `string` feature.
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- `answer2`: a `string` feature.
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- `answer3`: a `string` feature.
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- `label`: a `int32` feature.
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### Data Splits
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| name |train|validation|test|
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|-------|----:|---------:|---:|
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|default|25262| 2985|6963|
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## Dataset Creation
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### Curation Rationale
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the source language producers?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Annotations
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#### Annotation process
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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#### Who are the annotators?
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Personal and Sensitive Information
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Discussion of Biases
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Other Known Limitations
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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## Additional Information
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### Dataset Curators
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Licensing Information
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As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
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### Citation Information
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```
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@inproceedings{huang-etal-2019-cosmos,
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title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
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author = "Huang, Lifu and
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Le Bras, Ronan and
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Bhagavatula, Chandra and
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Choi, Yejin",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
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month = nov,
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year = "2019",
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address = "Hong Kong, China",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/D19-1243",
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doi = "10.18653/v1/D19-1243",
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pages = "2391--2401",
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}
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```
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### Contributions
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Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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cosmos_qa.py
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"""Cosmos QA dataset."""
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import csv
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import json
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import datasets
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_HOMEPAGE = "https://wilburone.github.io/cosmos/"
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_DESCRIPTION = """\
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Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
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"""
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_CITATION = """\
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@inproceedings{huang-etal-2019-cosmos,
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title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
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author = "Huang, Lifu and
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Le Bras, Ronan and
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Bhagavatula, Chandra and
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Choi, Yejin",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
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month = nov,
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year = "2019",
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address = "Hong Kong, China",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/D19-1243",
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doi = "10.18653/v1/D19-1243",
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pages = "2391--2401",
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}
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"""
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_LICENSE = "CC BY 4.0"
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_URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
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_URLS = {
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"train": _URL + "train.csv",
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"test": _URL + "test.jsonl",
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"dev": _URL + "valid.csv",
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}
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class CosmosQa(datasets.GeneratorBasedBuilder):
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"""Cosmos QA dataset."""
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|
47 |
-
VERSION = datasets.Version("0.1.0")
|
48 |
-
|
49 |
-
def _info(self):
|
50 |
-
return datasets.DatasetInfo(
|
51 |
-
description=_DESCRIPTION,
|
52 |
-
features=datasets.Features(
|
53 |
-
{
|
54 |
-
"id": datasets.Value("string"),
|
55 |
-
"context": datasets.Value("string"),
|
56 |
-
"question": datasets.Value("string"),
|
57 |
-
"answer0": datasets.Value("string"),
|
58 |
-
"answer1": datasets.Value("string"),
|
59 |
-
"answer2": datasets.Value("string"),
|
60 |
-
"answer3": datasets.Value("string"),
|
61 |
-
"label": datasets.Value("int32"),
|
62 |
-
}
|
63 |
-
),
|
64 |
-
homepage=_HOMEPAGE,
|
65 |
-
citation=_CITATION,
|
66 |
-
license=_LICENSE,
|
67 |
-
)
|
68 |
-
|
69 |
-
def _split_generators(self, dl_manager):
|
70 |
-
"""Returns SplitGenerators."""
|
71 |
-
urls_to_download = _URLS
|
72 |
-
dl_dir = dl_manager.download_and_extract(urls_to_download)
|
73 |
-
return [
|
74 |
-
datasets.SplitGenerator(
|
75 |
-
name=datasets.Split.TRAIN,
|
76 |
-
gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
|
77 |
-
),
|
78 |
-
datasets.SplitGenerator(
|
79 |
-
name=datasets.Split.TEST,
|
80 |
-
gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
|
81 |
-
),
|
82 |
-
datasets.SplitGenerator(
|
83 |
-
name=datasets.Split.VALIDATION,
|
84 |
-
gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
|
85 |
-
),
|
86 |
-
]
|
87 |
-
|
88 |
-
def _generate_examples(self, filepath, split):
|
89 |
-
"""Yields examples."""
|
90 |
-
with open(filepath, encoding="utf-8") as f:
|
91 |
-
if split == "test":
|
92 |
-
for id_, row in enumerate(f):
|
93 |
-
data = json.loads(row)
|
94 |
-
yield id_, {
|
95 |
-
"id": data["id"],
|
96 |
-
"context": data["context"],
|
97 |
-
"question": data["question"],
|
98 |
-
"answer0": data["answer0"],
|
99 |
-
"answer1": data["answer1"],
|
100 |
-
"answer2": data["answer2"],
|
101 |
-
"answer3": data["answer3"],
|
102 |
-
"label": int(data.get("label", -1)),
|
103 |
-
}
|
104 |
-
else:
|
105 |
-
data = csv.DictReader(f)
|
106 |
-
for id_, row in enumerate(data):
|
107 |
-
yield id_, {
|
108 |
-
"id": row["id"],
|
109 |
-
"context": row["context"],
|
110 |
-
"question": row["question"],
|
111 |
-
"answer0": row["answer0"],
|
112 |
-
"answer1": row["answer1"],
|
113 |
-
"answer2": row["answer2"],
|
114 |
-
"answer3": row["answer3"],
|
115 |
-
"label": int(row.get("label", -1)),
|
116 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
dataset_infos.json
DELETED
@@ -1 +0,0 @@
|
|
1 |
-
{"default": {"description": "Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context\n", "citation": "@inproceedings{huang-etal-2019-cosmos,\n title = \"Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning\",\n author = \"Huang, Lifu and\n Le Bras, Ronan and\n Bhagavatula, Chandra and\n Choi, Yejin\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D19-1243\",\n doi = \"10.18653/v1/D19-1243\",\n pages = \"2391--2401\",\n}\n", "homepage": "https://wilburone.github.io/cosmos/", "license": "CC BY 4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer0": {"dtype": "string", "id": null, "_type": "Value"}, "answer1": {"dtype": "string", "id": null, "_type": "Value"}, "answer2": {"dtype": "string", "id": null, "_type": "Value"}, "answer3": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cosmos_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17159918, "num_examples": 25262, "dataset_name": "cosmos_qa"}, "test": {"name": "test", "num_bytes": 5121479, "num_examples": 6963, "dataset_name": "cosmos_qa"}, "validation": {"name": "validation", "num_bytes": 2186987, "num_examples": 2985, "dataset_name": "cosmos_qa"}}, "download_checksums": {"https://github.com/wilburOne/cosmosqa/raw/master/data/train.csv": {"num_bytes": 16660449, "checksum": "d8d5ca1f9f6534b6530550718591af89372d976a8fc419360fab4158dee4d0b2"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/test.jsonl": {"num_bytes": 5610681, "checksum": "70005196dc2588b95de34f1657b25e2c1a4810cfe55b5bb0c0e15580c37b3ed0"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/valid.csv": {"num_bytes": 2128345, "checksum": "a6a94fc1463ca82bb10f98ef68ed535405e6f5c36e044ff8e136b5c19dea63f3"}}, "download_size": 24399475, "post_processing_size": null, "dataset_size": 24468384, "size_in_bytes": 48867859}}
|
|
|
|
default/cosmos_qa-test.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:82079c2d47a0b7c71e3f3c428e7a451b9a21886bcd4010a1e55c002177a86679
|
3 |
+
size 2873193
|
default/cosmos_qa-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2eec429c4a2385f615b65343121ed88deeac385a698a5ead185097065b338da0
|
3 |
+
size 7923049
|
default/cosmos_qa-validation.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3591b6e76f927e8ebbac6282ceaa5b3e8b36ce7107a438de8fbe2fabf2371229
|
3 |
+
size 1233336
|