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melayu_sabah.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.
+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 TASK_TO_SCHEMA, Licenses, Tasks
+
+_DATASETNAME = "melayu_sabah"
+
+
+_DESCRIPTION = """\
+Korpus Variasi Bahasa Melayu: Sabah is a language corpus sourced from various folklores in Melayu Sabah dialect.
+"""
+
+_CITATION = """\
+@misc{melayusabah,
+  author = {Hiroki Nomoto},
+  title = {Melayu_Sabah},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\\url{https://github.com/matbahasa/Melayu_Sabah}},
+  commit = {90a46c8268412ccc1f29cdcbbd47354474f12d50}
+}
+"""
+
+_HOMEPAGE = "https://github.com/matbahasa/Melayu_Sabah"
+
+
+_LANGUAGES = ["msi"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "sabah201701": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201701.txt",
+    "sabah201702": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201702.txt",
+    "sabah201901": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201901.txt",
+    "sabah201902": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201902.txt",
+    "sabah201903": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201903.txt",
+    "sabah201904": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201904.txt",
+    "sabah201905": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201905.txt",
+    "sabah201906": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201906.txt",
+    "sabah201907": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201907.txt",
+    "sabah201908": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201908.txt",
+    "sabah201909": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201909.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MelayuSabah(datasets.GeneratorBasedBuilder):
+    """Korpus Variasi Bahasa Melayu:
+    Sabah is a language corpus sourced from various folklores in Melayu Sabah dialect."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_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=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_ssp":
+            features = schemas.self_supervised_pretraining.features
+
+        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[key] for key in _URLS.keys()]
+        data_path = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_path[0], "split": "train", "other_path": data_path[1:]},
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str, other_path: List) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        filepaths = [filepath] + other_path
+        data = []
+        for filepath in filepaths[:2]:
+            with open(filepath, "r") as f:
+                sentences = [line.rstrip() for line in f.readlines()]
+                sentences = [sentence.split("\t")[-1] for sentence in sentences]
+                data.append("\n".join(sentences))
+
+        for filepath in filepaths[2:]:
+            with open(filepath, "r") as f:
+                data.append([line.rstrip() for line in f.readlines()])
+
+        for id, text in enumerate(data):
+            yield id, {"id": id, "text": text}