Upload glue.py

glue.py

@@ -0,0 +1,240 @@
+import os
+import json
+import datasets
+from datasets import BuilderConfig, Features, ClassLabel, Value, Sequence
+
+
+_DESCRIPTION = """
+# 한국어 지시학습 데이터셋
+- glue 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}
+}
+"""
+
+# glue
+_COLA_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "label": Value(dtype="int32"),
+    "sentence": Value(dtype="string"),    
+})
+
+def _parsing_cola(file_path):
+    with open(file_path, mode="r") as f:
+        dataset = json.load(f)
+    for _idx, data in enumerate(dataset):
+        _data_index_by_user = data["data_index_by_user"]
+        _label = data["label"]
+        _sentence = data["sentence"]
+
+        yield _idx, {
+            "data_index_by_user": _data_index_by_user,
+            "label": _label,
+            "sentence": _sentence
+        }
+
+_MRPC_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "sentence1": Value(dtype="string"),
+    "sentence2": Value(dtype="string"),
+    "label": Value(dtype="int32"),
+    "idx": Value(dtype="int32")
+})
+
+def _parsing_mrpc(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"]
+        _sentence1 = data["sentence1"]
+        _sentence2 = data["sentence2"]
+        _label = data["label"]
+        _idx = data["idx"]
+
+        yield _i, {
+            "data_index_by_user": _data_index_by_user,
+            "sentence1": _sentence1,
+            "sentence2": _sentence2,
+            "label": _label,
+            "idx": _idx,
+        }
+
+_QNLI_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "label": Value(dtype="int32"),
+    "question": Value(dtype="string"),
+    "sentence": Value(dtype="string"),
+})
+
+def _parsing_qnli(file_path):
+    with open(file_path, mode="r") as f:
+        dataset = json.load(f)
+    for _idx, data in enumerate(dataset):
+        _data_index_by_user = data["data_index_by_user"]
+        _label = data["label"]
+        _question = data["question"]
+        _sentence = data["sentence"]
+
+        yield _idx, {
+            "data_index_by_user": _data_index_by_user,
+            "label": _label,
+            "question": _question,
+            "sentence": _sentence,
+        }
+
+_QQP_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "question1": Value(dtype="string"),
+    "question2": Value(dtype="string"),
+    "label": Value(dtype="int32"),
+    "idx": Value(dtype="int32")
+})
+
+def _parsing_qqp(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"]
+        _question1 = data["question1"]
+        _question2 = data["question2"]
+        _label = data["label"]
+        _idx = data["idx"]
+
+        yield _i, {
+            "data_index_by_user": _data_index_by_user,
+            "question1": _question1,
+            "question2": _question2,
+            "label": _label,
+            "idx": _idx,
+        }
+
+_WNLI_FEATURES = Features({
+    "data_index_by_user": Value(dtype="int32"),
+    "sentence1": Value(dtype="string"),
+    "sentence2": Value(dtype="string"),
+    "label": Value(dtype="int32"),
+    "idx": Value(dtype="int32")
+})
+
+def _parsing_wnli(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"]
+        _sentence1 = data["sentence1"]
+        _sentence2 = data["sentence2"]
+        _label = data["label"]
+        _idx = data["idx"]
+
+        yield _i, {
+            "data_index_by_user": _data_index_by_user,
+            "sentence1": _sentence1,
+            "sentence2": _sentence2,
+            "label": _label,
+            "idx": _idx,
+        }
+
+class GlueConfig(BuilderConfig):
+    def __init__(self, name, feature, reading_fn, parsing_fn, citation, **kwargs):
+        super(GlueConfig, 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 GLUE(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        GlueConfig(
+            name = "cola",
+            data_dir = "./glue",
+            feature = _COLA_FEATURES,
+            reading_fn = _parsing_cola,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+        GlueConfig(
+            name = "mrpc",
+            data_dir = "./glue",
+            feature = _MRPC_FEATURES,
+            reading_fn = _parsing_mrpc,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+        GlueConfig(
+            name = "qnli",
+            data_dir = "./glue",
+            feature = _QNLI_FEATURES,
+            reading_fn = _parsing_qnli,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+        GlueConfig(
+            name = "qqp",
+            data_dir = "./glue",
+            feature = _QQP_FEATURES,
+            reading_fn = _parsing_qqp,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+        GlueConfig(
+            name = "wnli",
+            data_dir = "./glue",
+            feature = _WNLI_FEATURES,
+            reading_fn = _parsing_wnli,
+            parsing_fn = lambda x:x,
+            citation = _CITATION,
+        ),
+    ]
+    
+    def _info(self) -> datasets.DatasetInfo:
+        """Returns the dataset metadata."""
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=self.config.feature,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators"""
+        if self.config.name == "qqp":
+            path_kv = {
+                datasets.Split.TRAIN:[
+                    os.path.join(dl_manager.manual_dir, f"{self.config.name}/train.json")
+                ],
+            }
+        else:
+            path_kv = {
+                datasets.Split.TRAIN:[
+                    os.path.join(dl_manager.manual_dir, f"{self.config.name}/train.json")
+                ],
+                datasets.Split.VALIDATION:[
+                    os.path.join(dl_manager.manual_dir, f"{self.config.name}/validation.json")
+                ],
+                datasets.Split.TEST:[
+                    os.path.join(dl_manager.manual_dir, f"{self.config.name}/test.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)