Upload nq_open.py

nq_open.py

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+import os
+import json
+import datasets
+from datasets import BuilderConfig, Features, Value, Sequence
+
+
+_DESCRIPTION = """
+# 한국어 지시학습 데이터셋
+- nq_open 데이터셋을 한국어로 변역한 데이터셋
+"""
+
+_CITATION = """
+@inproceedings{KITD,
+  title={언어 번역 모델을 통한 한국어 지시 학습 데이터 세트 구축},
+  author={임영서, 추현창, 김산, 장진예, 정민영, 신사임},
+  booktitle={제 35회 한글 및 한국어 정보처리 학술대회},
+  pages={591--595},
+  month=oct,
+  year={2023}
+}
+"""
+
+def _list(data_list):
+    result = list()
+    for data in data_list:
+        result.append(data)
+    return result
+
+# nq_open
+_NQ_OPEN_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "question": Value(dtype="string"),
+    "answer": Sequence(Value(dtype="string"))
+})
+
+def _parsing_nq_open(file_path):
+    with open(file_path, mode="r") as f:
+        dataset = json.load(f)
+    for _i, data in enumerate(dataset):
+        _data_index_by_user = data["data_index_by_user"]
+        _question = data["question"]
+        _answer = _list(data["answer"])
+        
+        yield _i, {
+            "data_index_by_user": _data_index_by_user,
+            "question": _question,
+            "answer": _answer
+        }
+
+class Nq_openConfig(BuilderConfig):
+    def __init__(self, name, feature, reading_fn, parsing_fn, citation, **kwargs):
+        super(Nq_openConfig, self).__init__(
+            name = name,
+            version=datasets.Version("1.0.0"),
+            **kwargs)
+        self.feature = feature
+        self.reading_fn = reading_fn
+        self.parsing_fn = parsing_fn
+        self.citation = citation
+
+class NQ_OPEN(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        Nq_openConfig(
+            name = "base",
+            data_dir = "./nq_open",
+            feature = _NQ_OPEN_FEATURES,
+            reading_fn = _parsing_nq_open,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+    ]
+    
+    def _info(self) -> datasets.DatasetInfo:
+        """Returns the dataset metadata."""
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_NQ_OPEN_FEATURES,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators"""
+        path_kv = {
+            datasets.Split.TRAIN:[
+                os.path.join(dl_manager.manual_dir, f"train.json")
+            ],
+            datasets.Split.VALIDATION:[
+                os.path.join(dl_manager.manual_dir, f"validation.json")
+            ],
+        }
+        return [
+            datasets.SplitGenerator(name=k, gen_kwargs={"path_list": v})
+            for k, v in path_kv.items()
+        ]
+    
+    def _generate_examples(self, path_list):
+        """Yields examples."""
+        for path in path_list:
+            try:
+                for example in iter(self.config.reading_fn(path)):
+                    yield self.config.parsing_fn(example)
+            except Exception as e:
+                print(e)