Delete loading script

sberquad.py

@@ -1,104 +0,0 @@
-# coding=utf-8
-"""SberQUAD: Sber Question Answering Dataset."""
-
-import os
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """\
-@article{Efimov_2020,
-   title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
-   ISBN={9783030582197},
-   ISSN={1611-3349},
-   url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
-   DOI={10.1007/978-3-030-58219-7_1},
-   journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},
-   publisher={Springer International Publishing},
-   author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},
-   year={2020},
-   pages={3–15}
-}
- """
-
-
-_DESCRIPTION = """\
-Sber Question Answering Dataset (SberQuAD) is a reading comprehension \
-dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
-articles, where the answer to every question is a segment of text, or span, \
-from the corresponding reading passage, or the question might be unanswerable. \
-Russian original analogue presented in Sberbank Data Science Journey 2017.
-"""
-
-_URLS = {"train": os.path.join("data", "train_v1.0.json.gz"), "dev": os.path.join("data", "dev_v1.0.json.gz"), "test": os.path.join("data", "origin_test.json.gz")}
-
-
-class Sberquad(datasets.GeneratorBasedBuilder):
-    """SberQUAD: Sber Question Answering Dataset. Version 1.0."""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="sberquad", version=VERSION, description=_DESCRIPTION)]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="",
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for article in squad["data"]:
-                title = article.get("title", "")
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"]
-                    for qa in paragraph["qas"]:
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-                        yield key, {
-                            "title": title,
-                            "context": context,
-                            "question": qa["question"],
-                            "id": qa["id"],
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }
-                        key += 1