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indonglish.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 csv
+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 = """\
+@article{Astuti2023,
+title = {Code-Mixed Sentiment Analysis using Transformer for Twitter Social Media Data},
+journal = {International Journal of Advanced Computer Science and Applications},
+doi = {10.14569/IJACSA.2023.0141053},
+url = {http://dx.doi.org/10.14569/IJACSA.2023.0141053},
+year = {2023},
+publisher = {The Science and Information Organization},
+volume = {14},
+number = {10},
+author = {Laksmita Widya Astuti and Yunita Sari and Suprapto}
+}
+"""
+
+_DATASETNAME = "indonglish"
+_DESCRIPTION = """\
+Indonglish-dataset was constructed based on keywords derived from the
+sociolinguistic phenomenon observed among teenagers in South Jakarta. The
+dataset was designed to tackle the semantic task of sentiment analysis,
+incorporating three distinct label categories: positive, negative, and
+neutral. The annotation of the dataset was carried out by a panel of five
+annotators, each possessing expertise language and data science.
+"""
+
+_HOMEPAGE = "https://github.com/laksmitawidya/indonglish-dataset"
+_LANGUAGES = ["ind"]
+_LICENSE = Licenses.UNKNOWN.value
+_LOCAL = False
+
+_URLS = {
+    "skenario-orig": {
+        "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/train.csv",
+        "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/validation.csv",
+        "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario-ori/test.csv",
+    },
+    "skenario1": {
+        "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/training.csv",
+        "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/validation.csv",
+        "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario1/test.csv",
+    },
+    "skenario2": {
+        "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/training.csv",
+        "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/validation.csv",
+        "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario2/test.csv",
+    },
+    "skenario3": {
+        "train": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/training.csv",
+        "validation": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/validation.csv",
+        "test": "https://raw.githubusercontent.com/laksmitawidya/indonglish-dataset/master/skenario3/test.csv",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class Indonglish(datasets.GeneratorBasedBuilder):
+    """Indonglish dataset for sentiment analysis from https://github.com/laksmitawidya/indonglish-dataset."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+    _LABELS = ["Positif", "Negatif", "Netral"]
+
+    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,
+        ),
+    ]
+    for i in range(1, 4):
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_skenario{i}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}_skenario{i}",
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_skenario{i}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} SEACrowd schema",
+                schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+                subset_id=f"{_DATASETNAME}_skenario{i}",
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tweet": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self._LABELS),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self._LABELS)
+
+        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."""
+
+        if "skenario" in self.config.name:
+            setting = self.config.name.split("_")[1]
+        else:
+            setting = "skenario-orig"
+
+        data_paths = {
+            setting: {
+                "train": Path(dl_manager.download_and_extract(_URLS[setting]["train"])),
+                "validation": Path(dl_manager.download_and_extract(_URLS[setting]["validation"])),
+                "test": Path(dl_manager.download_and_extract(_URLS[setting]["test"])),
+            }
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_paths[setting]["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_paths[setting]["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_paths[setting]["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        # read csv file
+        with open(filepath, "r", encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file)
+            csv_data = [row for row in csv_reader]
+        csv_data = csv_data[1:]  # remove header
+
+        num_sample = len(csv_data)
+
+        for i in range(num_sample):
+            if self.config.schema == "source":
+                example = {
+                    "id": str(i),
+                    "tweet": csv_data[i][0],
+                    "label": csv_data[i][1],
+                }
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                example = {
+                    "id": str(i),
+                    "text": csv_data[i][0],
+                    "label": csv_data[i][1],
+                }
+
+            yield i, example