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README.md

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----
-annotations_creators:
-- crowdsourced
-language:
-- en
-language_creators:
-- found
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-pretty_name: CosmosQA
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- multiple-choice
-task_ids:
-- multiple-choice-qa
-paperswithcode_id: cosmosqa
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: context
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer0
-    dtype: string
-  - name: answer1
-    dtype: string
-  - name: answer2
-    dtype: string
-  - name: answer3
-    dtype: string
-  - name: label
-    dtype: int32
-  splits:
-  - name: train
-    num_bytes: 17159918
-    num_examples: 25262
-  - name: test
-    num_bytes: 5121479
-    num_examples: 6963
-  - name: validation
-    num_bytes: 2186987
-    num_examples: 2985
-  download_size: 24399475
-  dataset_size: 24468384
----
-
-# Dataset Card for "cosmos_qa"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://wilburone.github.io/cosmos/](https://wilburone.github.io/cosmos/)
-- **Repository:** https://github.com/wilburOne/cosmosqa/
-- **Paper:** [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277)
-- **Point of Contact:** [Lifu Huang](mailto:warrior.fu@gmail.com)
-- **Size of downloaded dataset files:** 23.27 MB
-- **Size of the generated dataset:** 23.37 MB
-- **Total amount of disk used:** 46.64 MB
-
-### Dataset Summary
-
-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
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### default
-
-- **Size of downloaded dataset files:** 23.27 MB
-- **Size of the generated dataset:** 23.37 MB
-- **Total amount of disk used:** 46.64 MB
-
-An example of 'validation' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "answer0": "If he gets married in the church he wo nt have to get a divorce .",
-    "answer1": "He wants to get married to a different person .",
-    "answer2": "He wants to know if he does nt like this girl can he divorce her ?",
-    "answer3": "None of the above choices .",
-    "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...",
-    "id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
-    "label": 1,
-    "question": "Why is this person asking about divorce ?"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `id`: a `string` feature.
-- `context`: a `string` feature.
-- `question`: a `string` feature.
-- `answer0`: a `string` feature.
-- `answer1`: a `string` feature.
-- `answer2`: a `string` feature.
-- `answer3`: a `string` feature.
-- `label`: a `int32` feature.
-
-### Data Splits
-
-| name  |train|validation|test|
-|-------|----:|---------:|---:|
-|default|25262|      2985|6963|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
-
-### Citation Information
-
-```
-@inproceedings{huang-etal-2019-cosmos,
-    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
-    author = "Huang, Lifu  and
-      Le Bras, Ronan  and
-      Bhagavatula, Chandra  and
-      Choi, Yejin",
-    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)",
-    month = nov,
-    year = "2019",
-    address = "Hong Kong, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D19-1243",
-    doi = "10.18653/v1/D19-1243",
-    pages = "2391--2401",
-}
-```
-
-
-### Contributions
-
-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.

cosmos_qa.py

@@ -1,116 +0,0 @@
-"""Cosmos QA dataset."""
-
-
-import csv
-import json
-
-import datasets
-
-
-_HOMEPAGE = "https://wilburone.github.io/cosmos/"
-
-_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
-"""
-
-_CITATION = """\
-@inproceedings{huang-etal-2019-cosmos,
-    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
-    author = "Huang, Lifu  and
-      Le Bras, Ronan  and
-      Bhagavatula, Chandra  and
-      Choi, Yejin",
-    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)",
-    month = nov,
-    year = "2019",
-    address = "Hong Kong, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/D19-1243",
-    doi = "10.18653/v1/D19-1243",
-    pages = "2391--2401",
-}
-"""
-
-_LICENSE = "CC BY 4.0"
-
-_URL = "https://github.com/wilburOne/cosmosqa/raw/master/data/"
-_URLS = {
-    "train": _URL + "train.csv",
-    "test": _URL + "test.jsonl",
-    "dev": _URL + "valid.csv",
-}
-
-
-class CosmosQa(datasets.GeneratorBasedBuilder):
-    """Cosmos QA dataset."""
-
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answer0": datasets.Value("string"),
-                    "answer1": datasets.Value("string"),
-                    "answer2": datasets.Value("string"),
-                    "answer3": datasets.Value("string"),
-                    "label": datasets.Value("int32"),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = _URLS
-        dl_dir = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            if split == "test":
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    yield id_, {
-                        "id": data["id"],
-                        "context": data["context"],
-                        "question": data["question"],
-                        "answer0": data["answer0"],
-                        "answer1": data["answer1"],
-                        "answer2": data["answer2"],
-                        "answer3": data["answer3"],
-                        "label": int(data.get("label", -1)),
-                    }
-            else:
-                data = csv.DictReader(f)
-                for id_, row in enumerate(data):
-                    yield id_, {
-                        "id": row["id"],
-                        "context": row["context"],
-                        "question": row["question"],
-                        "answer0": row["answer0"],
-                        "answer1": row["answer1"],
-                        "answer2": row["answer2"],
-                        "answer3": row["answer3"],
-                        "label": int(row.get("label", -1)),
-                    }

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

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