Upload tquad2.py

The config and dataset classes. Without this file, load_dataset function raises a Dataset Generation error.

tquad2.py

@@ -0,0 +1,116 @@
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+_CITATION = ""
+
+_DESCRIPTION = ""
+
+_URL = "https://huggingface.co/datasets/husnu/tquad2/raw/main/"
+_URLS = {
+    "train": _URL + "tquad_train_data_v2.json",
+    "dev": _URL + "tquad_dev_data_v2.json",
+}
+
+
+class TQuAD2Config(datasets.BuilderConfig):
+    """BuilderConfig for TQuAD2."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TQuAD2.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TQuAD2Config, self).__init__(**kwargs)
+
+
+class TQuAD2(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        TQuAD2Config(name="tquad2", version=datasets.Version("2.0.0"), description="TQuAD2 dataset"),
+    ]
+
+    IDS_ = []
+
+    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("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://huggingface.co/datasets/husnu/tquad2",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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."""
+        with open(filepath, encoding="utf-8") as f:
+            squad = json.load(f)
+            for example in squad["data"]:
+                title = example.get("title", "")
+                for paragraph in example["paragraphs"]:
+                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
+                    for qa in paragraph["qas"]:
+                        question = qa["question"]
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"] for answer in qa["answers"]]
+
+                        # if id_ is already in the dataset, we skip it
+                        while id_ in self.IDS_:
+                            if isinstance(id_, int):
+                                id_ = id_ + 1
+                            else:
+                                id_ = id_ + "_duplicate"
+
+                        self.IDS_.append(id_)
+
+                        # 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,
+                            },
+                        }