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uit_vion.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{fujita2021empirical,
+  title={An Empirical Investigation of Online News Classification on an Open-Domain, Large-Scale and High-Quality Dataset in Vietnamese},
+  author={Fujita, H and Perez-Meana, H},
+  booktitle={New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT_21)},
+  volume={337},
+  pages={367},
+  year={2021},
+  organization={IOS Press}
+}
+"""
+
+_DATASETNAME = "uit_vion"
+
+
+_DESCRIPTION = """\
+UIT-ViON (Vietnamese Online Newspaper) is a dataset collected from well-known online newspapers in Vietnamese.
+The UIT-ViON is an open-domain, large-scale, and high-quality dataset consisting of 260,000 textual data
+points annotated with 13 different categories for evaluating Vietnamese short text classification.
+The dataset is split into training, validation, and test sets, each containing 208000, 26000,
+and 26000 pieces of text, respectively.
+"""
+
+_HOMEPAGE = "https://github.com/kh4nh12/UIT-ViON-Dataset"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/kh4nh12/UIT-ViON-Dataset/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.TOPIC_MODELING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UitVion(datasets.GeneratorBasedBuilder):
+    """UIT-ViON (Vietnamese Online Newspaper) is a dataset collected from well-known online newspapers in Vietnamese."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    LABEL_CLASSES = [i for i in range(13)]
+
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "link": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self.LABEL_CLASSES),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self.LABEL_CLASSES)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        file_dir = os.path.join("UIT-ViON-Dataset-main", "data.zip")
+        data_dir = os.path.join(data_dir, file_dir)
+        data_dir = dl_manager.download_and_extract(data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "UIT-ViON_train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "UIT-ViON_test.csv"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "UIT-ViON_dev.csv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data = pd.read_csv(filepath)
+
+        if self.config.schema == "source":
+            for i, row in data.iterrows():
+                yield i, {
+                    "title": str(row["title"]),
+                    "link": str(row["link"]),
+                    "label": row["label"],
+                }
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in data.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "text": str(row["title"]),
+                    "label": int(row["label"]),
+                }