Convert dataset to Parquet

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - crowdsourced
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - cc-by-4.0
 multilinguality:
 - monolingual
-pretty_name: CosmosQA
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -19,6 +18,7 @@ task_categories:
 task_ids:
 - multiple-choice-qa
 paperswithcode_id: cosmosqa
+pretty_name: CosmosQA
 dataset_info:
   features:
   - name: id
@@ -39,16 +39,25 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 17159918
+    num_bytes: 17156676
     num_examples: 25262
   - name: test
-    num_bytes: 5121479
+    num_bytes: 5120580
     num_examples: 6963
   - name: validation
-    num_bytes: 2186987
+    num_bytes: 2186585
     num_examples: 2985
-  download_size: 24399475
-  dataset_size: 24468384
+  download_size: 12029581
+  dataset_size: 24463841
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for "cosmos_qa"

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)),
-                    }

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:4477336b3eb4fed17dd14d0b4932d758b22ee4f0c5cda1c853ebb30612c92c8f
+size 2873194

data/train-00000-of-00001.parquet

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+oid sha256:f7b9516f6650bb92b12c02cf78c7ffcc31659546e38ba82bb3f10ff84c4e6d98
+size 7923050

data/validation-00000-of-00001.parquet

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+oid sha256:bc6e093c9b05d7b97a74f587c32c5501718317d9ae4c6046cae92878197c3929
+size 1233337