Delete loading script

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)),
-                    }