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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
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+poquad-train.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- pl
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: PoQuaD
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- open-domain-qa
+---
+
+PoQuaD dataset

poquad-train.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1ac3acabb49fedb7bb7db0de0690ddb22585d6419321589cc1bb0a8068a4ff9
+size 47183344

poquad.py

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+"""FROM SQUAD_V2"""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+# TODO(squad_v2): BibTeX citation
+_CITATION = """\
+Tuora, R., Zawadzka-Paluektau, N., Klamra, C., Zwierzchowska, A., Kobyliński, Ł. (2022). 
+Towards a Polish Question Answering Dataset (PoQuAD). 
+In: Tseng, YH., Katsurai, M., Nguyen, H.N. (eds) From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries. ICADL 2022. 
+Lecture Notes in Computer Science, vol 13636. Springer, Cham. 
+https://doi.org/10.1007/978-3-031-21756-2_16
+"""
+
+_DESCRIPTION = """\
+PoQuaD description
+"""
+
+
+_URLS = {
+    "train": "poquad-train.json",
+    "dev": "poquad-dev.json",
+}
+
+
+class SquadV2Config(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUADV2.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadV2Config, self).__init__(**kwargs)
+
+
+class SquadV2(datasets.GeneratorBasedBuilder):
+    """TODO(squad_v2): Short description of my dataset."""
+
+    # TODO(squad_v2): Set up version.
+    BUILDER_CONFIGS = [
+        SquadV2Config(name="poquad", version=datasets.Version("1.0.0"), description="PoQuaD plaint text"),
+    ]
+
+    def _info(self):
+        # TODO(squad_v2): Specifies the datasets.DatasetInfo object
+        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("string"),
+                    "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"),
+                        }
+                    ),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(squad_v2): 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["dev"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(squad_v2): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            squad = json.load(f)
+            id_ = 0
+            for example in squad["data"]:
+                title = example.get("title", "")
+                # paragraph_id = example["id"]
+                for paragraph in example["paragraphs"]:
+                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
+                    for qa in paragraph["qas"]:
+                        question = qa["question"]
+                        
+                        if "answers" not in qa:
+                            continue
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        #answer_ends = [answer["answer_end"] for answer in qa["answers"]]
+                        answers = [answer["text"] for answer in qa["answers"]]
+                        is_impossible = qa["is_impossible"]
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        id_ += 1
+                        yield str(id_), {
+                            "id": str(id_),
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "is_impossible" : is_impossible,
+                            # "paragraph_id": paragraph_id,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                #"answer_end": answer_ends,
+                                "text": answers,
+                            },
+                        }