Delete loading script

coqa.py

@@ -1,91 +0,0 @@
-"""CoQA dataset."""
-
-
-import json
-
-import datasets
-
-
-_HOMEPAGE = "https://stanfordnlp.github.io/coqa/"
-
-_CITATION = """\
-@article{reddy-etal-2019-coqa,
-    title = "{C}o{QA}: A Conversational Question Answering Challenge",
-    author = "Reddy, Siva  and
-      Chen, Danqi  and
-      Manning, Christopher D.",
-    journal = "Transactions of the Association for Computational Linguistics",
-    volume = "7",
-    year = "2019",
-    address = "Cambridge, MA",
-    publisher = "MIT Press",
-    url = "https://aclanthology.org/Q19-1016",
-    doi = "10.1162/tacl_a_00266",
-    pages = "249--266",
-}
-"""
-
-_DESCRIPTION = """\
-CoQA: A Conversational Question Answering Challenge
-"""
-
-_TRAIN_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json"
-_DEV_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json"
-
-
-class Coqa(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "source": datasets.Value("string"),
-                    "story": datasets.Value("string"),
-                    "questions": datasets.features.Sequence(datasets.Value("string")),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "input_text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                            "answer_end": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {"train": _TRAIN_DATA_URL, "dev": _DEV_DATA_URL}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"}
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for row in data["data"]:
-                questions = [question["input_text"] for question in row["questions"]]
-                story = row["story"]
-                source = row["source"]
-                answers_start = [answer["span_start"] for answer in row["answers"]]
-                answers_end = [answer["span_end"] for answer in row["answers"]]
-                answers = [answer["input_text"] for answer in row["answers"]]
-                yield row["id"], {
-                    "source": source,
-                    "story": story,
-                    "questions": questions,
-                    "answers": {"input_text": answers, "answer_start": answers_start, "answer_end": answers_end},
-                }