Upload quail.py

quail.py

@@ -0,0 +1,144 @@
+import os
+import json
+import datasets
+from datasets import BuilderConfig, Features, Value, Sequence
+
+
+_DESCRIPTION = """
+# 한국어 지시학습 데이터셋
+- quail 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},
+  year={2023}
+}
+@inproceedings{KITD,
+  title={Korean Instruction Tuning Dataset},
+  author={Yeongseo Lim, HyeonChang Chu, San Kim, Jin Yea Jang, Minyoung Jung, Saim Shin},
+  booktitle={Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology},
+  pages={591--595},
+  year={2023}
+}
+"""
+
+def _list(data_list):
+    result = list()
+    for data in data_list:
+        result.append(data)
+    return result
+
+# quail
+_QUAIL_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "id": Value(dtype="string"),
+    "context_id": Value(dtype="string"),
+    "question_id": Value(dtype="string"),
+    "domain": Value(dtype="string"),
+    "metadata": {
+        "author": Value(dtype="string"),
+        "title": Value(dtype="string"),
+        "url": Value(dtype="string"),
+    },
+    "context": Value(dtype="string"),
+    "question": Value(dtype="string"),
+    "question_type": Value(dtype="string"),
+    "answers": Sequence(Value(dtype="string")),
+    "correct_answer_id": Value(dtype="int32"),
+})
+
+def _parsing_quail(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"]
+        _id = data["id"]
+        _context_id = data["context_id"]
+        _question_id = data["question_id"]
+        _domain = data["domain"]
+        _metadata = {
+            "author": data["metadata"]["author"],
+            "title": data["metadata"]["title"],
+            "url": data["metadata"]["url"]
+        }
+        _context = data["context"]
+        _question = data["question"]
+        _question_type = data["question_type"]
+        _answers = _list(data["_answers"])
+        _correct_answer_id = data["correct_answer_id"]
+        
+        yield _i, {
+            "data_index_by_user": _data_index_by_user,
+            "id": _id,
+            "context_id": _context_id,
+            "question_id": _question_id,
+            "domain": _domain,
+            "metadata": _metadata,
+            "context": _context,
+            "question": _question,
+            "question_type": _question_type,
+            "answers": _answers,
+            "correct_answer_id": _correct_answer_id,
+        }
+
+class QuailConfig(BuilderConfig):
+    def __init__(self, name, feature, reading_fn, parsing_fn, citation, **kwargs):
+        super(QuailConfig, 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 QUAIL(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        QuailConfig(
+            name = "base",
+            data_dir = "./quail",
+            feature = _QUAIL_FEATURES,
+            reading_fn = _parsing_quail,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+    ]
+    
+    def _info(self) -> datasets.DatasetInfo:
+        """Returns the dataset metadata."""
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_QUAIL_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")
+            ],
+            "challenge":[
+                os.path.join(dl_manager.manual_dir, f"challenge.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)