Delete question_answering.py

question_answering.py

@@ -1,91 +0,0 @@
-import json
-import datasets
-
-_DESCRIPTION = """\\\\\\\\
-This dataset is a reading comprehension dataset on Persian Wikipedia. 
-The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer".
-"""
-
-_URL = "https://raw.githubusercontent.com/MatinChangiz/Question_answering/main/dataset/"
-_URLS = {
-    "train": _URL + "pqa_train.json",
-    "test": _URL + "pqa_test.json",
-}
-
-class QuestionAnsweringConfig(datasets.BuilderConfig):
-    """BuilderConfig for dataset."""
-    def __init__(self, **kwargs):
-        """BuilderConfig for dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(QuestionAnsweringConfig, self).__init__(**kwargs)
-
-class datasetQA(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        QuestionAnsweringConfig(name="dataset_qa", version=datasets.Version("1.0.0"), description="datasetQA version 1"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            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 of the dataset for documentation
-            homepage="https://github.com/MatinChangiz/Question_answering",
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(dataset_qa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(dataset_qa): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            print(filepath)
-            squad = json.load(f)
-            for example in squad["data"]:
-                title = example.get("title", "").strip()
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-                        
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }