Update qag_koquad.py

qag_koquad.py

@@ -0,0 +1,79 @@
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_VERSION = "0.0.0"
+_NAME = "qag_koquad"
+_CITATION = """
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+_DESCRIPTION = """Question & answer generation dataset based on SQuAD."""
+_URL = f"https://huggingface.co/datasets/lmqg/{_NAME}/resolve/main/data/processed"
+_URLS = {
+    'train': f'{_URL}/train.jsonl',
+    'test': f'{_URL}/test.jsonl',
+    'validation': f'{_URL}/validation.jsonl'
+}
+
+
+class QAGKOQuADConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(QAGKOQuADConfig, self).__init__(**kwargs)
+
+
+class QAGKOQuAD(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        QAGKOQuADConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
+    ]
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "answers": datasets.Sequence(datasets.Value("string")),
+                    "questions": datasets.Sequence(datasets.Value("string")),
+                    "paragraph": datasets.Value("string"),
+                    "questions_answers": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/asahi417/lm-question-generation"
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        _key = 0
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            _list = f.read().split('\n')
+            if _list[-1] == '':
+                _list = _list[:-1]
+            for i in _list:
+                data = json.loads(i)
+                yield _key, data
+                _key += 1