Delete loading script

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", ""),
-                }