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id_newspaper_2018.py

@@ -0,0 +1,150 @@
+# 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 json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@misc{feryandi2018,
+  author={Nurdiantoro, Feryandi}
+  title={Dataset-Artikel},
+  year = {2018},
+  url = {https://github.com/feryandi/Dataset-Artikel},
+}
+"""
+
+_DATASETNAME = "id_newspaper_2018"
+
+_DESCRIPTION = """\
+The ID Newspapers 2018 dataset provides 500K articles from various Indonesian news sources. Articles were taken from
+7 primary sources (Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika, Poskota). The compressed files can be
+retrieved from datahttps://huggingface.co/datasets/indonesian-nlp/id_newspapers_2018.
+"""
+
+_HOMEPAGE = "https://github.com/feryandi/Dataset-Artikel"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = "https://huggingface.co/datasets/indonesian-nlp/id_newspapers_2018/resolve/main/newspapers-json.tgz"
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IDNewspapers2018Dataset(datasets.GeneratorBasedBuilder):
+    """
+    ID Newspapers 2018 is a pretraining dataset from https://huggingface.co/datasets/indonesian-nlp/id_newspapers_2018.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_ssp",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_ssp",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"url": datasets.Value("string"), "date": datasets.Value("string"), "title": datasets.Value("string"), "content": datasets.Value("string")})
+        elif self.config.schema == "seacrowd_ssp":
+            features = schemas.ssp_features
+        else:
+            raise ValueError(f"Invalid schema: '{self.config.schema}'")
+
+        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.
+        """
+
+        path = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "path": path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, path: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+        file_paths = []
+        for path, subdirs, files in os.walk(path):
+            for name in files:
+                if name[-5:] == ".json":
+                    file_paths.append(os.path.join(path, name))
+
+        for idx, file_path in enumerate(file_paths):
+            with open(file_path, "r", encoding="utf-8") as file:
+                data = json.load(file)
+
+                if self.config.schema == "source":
+                    x = {
+                        "url": data["url"],
+                        "date": data["date"],
+                        "title": data["title"],
+                        "content": data["content"],
+                    }
+                    yield idx, x
+
+                elif self.config.schema == "seacrowd_ssp":
+                    x = {
+                        "id": str(idx),
+                        "text": data["content"],
+                    }
+                    yield idx, x
+
+                else:
+                    raise ValueError(f"Invalid schema: '{self.config.schema}'")