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identifikasi_bahasa.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
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{Tuhenay2021,
+  title = {Perbandingan Klasifikasi Bahasa Menggunakan Metode Naïve Bayes Classifier (NBC) Dan Support Vector Machine (SVM)},
+  volume = {4},
+  ISSN = {2656-1948},
+  url = {http://dx.doi.org/10.33387/jiko.v4i2.2958},
+  DOI = {10.33387/jiko.v4i2.2958},
+  number = {2},
+  journal = {JIKO (Jurnal Informatika dan Komputer)},
+  publisher = {LPPM Universitas Khairun},
+  author = {Tuhenay,  Deglorians},
+  year = {2021},
+  month = aug,
+  pages = {105-111}
+}
+"""
+
+_DATASETNAME = "identifikasi_bahasa"
+
+_DESCRIPTION = """\
+The identifikasi-bahasa dataset includes text samples in Indonesian, Ambonese, and Javanese. \
+Each entry is comprised of cleantext, representing the sentence content, and a label identifying the language. \
+The manual input process involved grouping the data by language categories, \
+with labels for language identification and cleantext representing sentence content. The dataset, excluding punctuation and numbers, \
+consists of a minimum of 3,000 Ambonese, 10,000 Javanese, \
+and 3,500 Indonesian language entries, meeting the research's minimum standard for effective language identification.
+"""
+
+_HOMEPAGE = "https://github.com/joanitolopo/identifikasi-bahasa"
+_LANGUAGES = ["ind", "jav", "abs"]
+
+_LICENSE = Licenses.APACHE_2_0.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/joanitolopo/identifikasi-bahasa/raw/main/DataKlasifikasi.xlsx",
+}
+
+_SUPPORTED_TASKS = [Tasks.LANGUAGE_IDENTIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+_TAGS = ["Ambon", "Indo", "Jawa"]
+
+
+class IdentifikasiBahasaDataset(datasets.GeneratorBasedBuilder):
+    """The "identifikasi-bahasa" dataset, manually grouped by language, \
+    contains labeled Indonesian, Ambonese, and Javanese text entries, excluding \
+    punctuation and numbers, with a minimum of 3,000 Ambonese, 10,000 Javanese, \
+    and 3,500 Indonesian entries for effective language identification."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    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({"cleanText": datasets.Value("string"), "label": datasets.Value("string")})
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(_TAGS)
+
+        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(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        dataset = pd.read_excel(filepath)
+
+        if self.config.schema == "source":
+            for i, row in dataset.iterrows():
+                yield i, {"cleanText": row["cleanText"], "label": row["label"]}
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset.iterrows():
+                yield i, {"id": i, "text": row["cleanText"], "label": row["label"]}