Delete loading script

covid_qa_deepset.py

@@ -1,127 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""COVID-QA: A Question Answering Dataset for COVID-19."""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{moller2020covid,
-  title={COVID-QA: A Question Answering Dataset for COVID-19},
-  author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
-  booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
-  year={2020}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical \
-experts on scientific articles related to COVID-19.
-"""
-
-_HOMEPAGE = "https://github.com/deepset-ai/COVID-QA"
-
-_LICENSE = "Apache License 2.0"
-
-_URL = "https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/"
-_URLs = {"covid_qa_deepset": _URL + "COVID-QA.json"}
-
-
-class CovidQADeepset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="covid_qa_deepset", version=VERSION, description="COVID-QA deepset"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "document_id": datasets.Value("int32"),
-                "context": datasets.Value("string"),
-                "question": datasets.Value("string"),
-                "is_impossible": datasets.Value("bool"),
-                "id": datasets.Value("int32"),
-                "answers": datasets.features.Sequence(
-                    {
-                        "text": datasets.Value("string"),
-                        "answer_start": datasets.Value("int32"),
-                    }
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        url = _URLs[self.config.name]
-        downloaded_filepath = dl_manager.download_and_extract(url)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": downloaded_filepath},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            covid_qa = json.load(f)
-            for article in covid_qa["data"]:
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    document_id = paragraph["document_id"]
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        is_impossible = qa["is_impossible"]
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "document_id": document_id,
-                            "context": context,
-                            "question": question,
-                            "is_impossible": is_impossible,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }