Create pquad.py

pquad.py

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+# Taken from https://huggingface.co/datasets/Shayanvsf/pquad_public/raw/main/pquad_public.py
+# Edited for the complete dataset (22MB Train.csv)
+# By Gholamreza Dar
+# Feb 2023
+
+import json
+import datasets
+_CITATION = """\
+@article{darvishi2022pquad,
+  title={PQuAD: A Persian Question Answering Dataset},
+  author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh},
+  journal={arXiv preprint arXiv:2202.06219},
+  year={2022}
+}
+"""
+_DESCRIPTION = """\\\\
+PQuAD: PQuAD is a crowd-sourced reading comprehension dataset on Persian Language.
+"""
+_URL = "https://raw.githubusercontent.com/AUT-NLP/PQuAD/main/Dataset/"
+_URLS = {
+    "train": _URL + "Train.json",
+    "validation":_URL + "Validation.json",
+    "test": _URL + "Test.json",
+}
+class pquad_public_Config(datasets.BuilderConfig):
+    """BuilderConfig for PQuAD."""
+    def __init__(self, **kwargs):
+        """BuilderConfig for PQuAD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(pquad_public_Config, self).__init__(**kwargs)
+class pquad_public(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        pquad_public_Config(name="pquad", version=datasets.Version("1.0.0"), description="PQuAD"),
+    ]
+    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("float64"),
+                    "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/AUT-NLP/PQuAD",
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(persian_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["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
+            ]
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(persian_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"]]
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }