Convert dataset to Parquet

README.md

@@ -9,7 +9,6 @@ license:
 - mit
 multilinguality:
 - monolingual
-pretty_name: CommonsenseQA
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -19,6 +18,7 @@ task_categories:
 task_ids:
 - open-domain-qa
 paperswithcode_id: commonsenseqa
+pretty_name: CommonsenseQA
 dataset_info:
   features:
   - name: id
@@ -37,16 +37,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 2209044
+    num_bytes: 2207794
     num_examples: 9741
   - name: validation
-    num_bytes: 274033
+    num_bytes: 273848
     num_examples: 1221
   - name: test
-    num_bytes: 258017
+    num_bytes: 257842
     num_examples: 1140
-  download_size: 4680691
-  dataset_size: 2741094
+  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-*
 ---
 
 # Dataset Card for "commonsense_qa"

commonsense_qa.py

@@ -1,102 +0,0 @@
-"""CommonsenseQA dataset."""
-
-
-import json
-
-import datasets
-
-
-_HOMEPAGE = "https://www.tau-nlp.org/commonsenseqa"
-
-_DESCRIPTION = """\
-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 paper for details.
-"""
-
-_CITATION = """\
-@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",
-}
-"""
-
-_URL = "https://s3.amazonaws.com/commensenseqa"
-_URLS = {
-    "train": f"{_URL}/train_rand_split.jsonl",
-    "validation": f"{_URL}/dev_rand_split.jsonl",
-    "test": f"{_URL}/test_rand_split_no_answers.jsonl",
-}
-
-
-class CommonsenseQa(datasets.GeneratorBasedBuilder):
-    """CommonsenseQA dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "question": datasets.Value("string"),
-                "question_concept": datasets.Value("string"),
-                "choices": datasets.features.Sequence(
-                    {
-                        "label": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                    }
-                ),
-                "answerKey": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        filepaths = dl_manager.download_and_extract(_URLS)
-        splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={
-                    "filepath": filepaths[split],
-                },
-            )
-            for split in splits
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for uid, row in enumerate(f):
-                data = json.loads(row)
-                choices = data["question"]["choices"]
-                labels = [label["label"] for label in choices]
-                texts = [text["text"] for text in choices]
-                yield uid, {
-                    "id": data["id"],
-                    "question": data["question"]["stem"],
-                    "question_concept": data["question"]["question_concept"],
-                    "choices": {"label": labels, "text": texts},
-                    "answerKey": data.get("answerKey", ""),
-                }

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19efe93223d1712397aaa44b3adc07f4fa50349206a611ca4f33afbec661fa5e
+size 151227

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0449767ed986bfc2ca52b1244a46ef12f732756727f3cb0a4ab69ac8b3d282b
+size 1247103

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdbd9bf9cc4d2349b24901038b2ab2f58e10e4e507ad2fd425dca55cd3cb6660
+size 160240

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge\nto predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.\nThe dataset is provided in two major training/validation/testing set splits: \"Random split\" which is the main evaluation\nsplit, and \"Question token split\", see paper for details.\n", "citation": "@inproceedings{talmor-etal-2019-commonsenseqa,\n    title = \"{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge\",\n    author = \"Talmor, Alon  and\n      Herzig, Jonathan  and\n      Lourie, Nicholas  and\n      Berant, Jonathan\",\n    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)\",\n    month = jun,\n    year = \"2019\",\n    address = \"Minneapolis, Minnesota\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/N19-1421\",\n    doi = \"10.18653/v1/N19-1421\",\n    pages = \"4149--4158\",\n    archivePrefix = \"arXiv\",\n    eprint        = \"1811.00937\",\n    primaryClass  = \"cs\",\n}\n", "homepage": "https://www.tau-nlp.org/commonsenseqa", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_concept": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"label": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "answerKey": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "commonsense_qa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2209044, "num_examples": 9741, "dataset_name": "commonsense_qa"}, "validation": {"name": "validation", "num_bytes": 274033, "num_examples": 1221, "dataset_name": "commonsense_qa"}, "test": {"name": "test", "num_bytes": 258017, "num_examples": 1140, "dataset_name": "commonsense_qa"}}, "download_checksums": {"https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl": {"num_bytes": 3785890, "checksum": "58ffa3c8472410e24b8c43f423d89c8a003d8284698a6ed7874355dedd09a2fb"}, "https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl": {"num_bytes": 471653, "checksum": "3210497fdaae614ac085d9eb873dd7f4d49b6f965a93adadc803e1229fd8a02a"}, "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl": {"num_bytes": 423148, "checksum": "b426896d71a9cd064cf01cfaf6e920817c51701ef66028883ac1af2e73ad5f29"}}, "download_size": 4680691, "post_processing_size": null, "dataset_size": 2741094, "size_in_bytes": 7421785}}